Статті в журналах: "Human Activity Prediction"

1

Dönmez, İlknur. "Human Activity Analysis and Prediction Using Google n-Grams." International Journal of Future Computer and Communication 7, no. 2 (June 2018): 32–36. http://dx.doi.org/10.18178/ijfcc.2018.7.2.516.

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2

Yan, Aixia, Zhi Wang, Jiaxuan Li, and Meng Meng. "Human Oral Bioavailability Prediction of Four Kinds of Drugs." International Journal of Computational Models and Algorithms in Medicine 3, no. 4 (October 2012): 29–42. http://dx.doi.org/10.4018/ijcmam.2012100104.

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In the development of drugs intended for oral use, good drug absorption and appropriate drug delivery are very important. Now the predictions for drug absorption and oral bioavailability follow similar approach: calculate molecular descriptors for molecules and build the prediction models. This approach works well for the prediction of compounds which cross a cell membrane from a region of high concentration to one of low concentration, but it does not work very well for the prediction of oral bioavailability, which represents the percentage of an oral dose which is able to produce a pharmacological activity. The models for bioavailability had limited predictability because there are a variety of pharmacokinetic factors influencing human oral bioavailability. Recent study has shown that good quantitative relationship could be obtained for subsets of drugs, such as those that have similar structure or the same pharmacological activity, or those that exhibit similar absorption and metabolism mechanisms. In this work, using MLR (Multiple Linear Regression) and SVM (Support Vector Machine), quantitative bioavailability prediction models were built for four kinds of drugs, which are Angiotensin Converting Enzyme Inhibitors or Angiotensin II Receptor Antagonists, Calcium Channel Blockers, Sodium and Potassium Channels Blockers and Quinolone Antimicrobial Agents. Explorations into subsets of compounds were performed and reliable prediction models were built for these four kinds of drugs. This work represents an exploration in predicting human oral bioavailability and could be used in other dataset of compounds with the same pharmacological activity.

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3

D., Manju, and Radha V. "A survey on human activity prediction techniques." International Journal of Advanced Technology and Engineering Exploration 5, no. 47 (October 21, 2018): 400–406. http://dx.doi.org/10.19101/ijatee.2018.547006.

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4

Keshinro, Babatunde, Younho Seong, and Sun Yi. "Deep Learning-based human activity recognition using RGB images in Human-robot collaboration." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (September 2022): 1548–53. http://dx.doi.org/10.1177/1071181322661186.

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In human-robot interaction, to ensure safety and effectiveness, robots need to be able to accurately predict human intentions. Hidden Markov Model, Bayesian Filtering, and deep learning methods have been used to predict human intentions. However, few studies have explored deep learning methods to predict variant human intention. Our study aims to evaluate the performance of the human intent recognition inference algorithm, and its impact on the human-robot team for collaborative tasks. Two deep learning algorithms ConvLSTM and LRCN were used to predict human intention. A dataset of 10 participants performing Pick, Throw, Wave, and Carry actions was used. The ConvLSTM method had a prediction accuracy of 74%. The LRCN method had a lower prediction accuracy of 25% compared to ConvLSTM. This result shows that deep learning methods using RGB images can predict human intent with high accuracy. The proposed method is successful in predicting human intents underlying human behavior.

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5

Bragança, Hendrio, Juan G. Colonna, Horácio A. B. F. Oliveira, and Eduardo Souto. "How Validation Methodology Influences Human Activity Recognition Mobile Systems." Sensors 22, no. 6 (March 18, 2022): 2360. http://dx.doi.org/10.3390/s22062360.

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In this article, we introduce explainable methods to understand how Human Activity Recognition (HAR) mobile systems perform based on the chosen validation strategies. Our results introduce a new way to discover potential bias problems that overestimate the prediction accuracy of an algorithm because of the inappropriate choice of validation methodology. We show how the SHAP (Shapley additive explanations) framework, used in literature to explain the predictions of any machine learning model, presents itself as a tool that can provide graphical insights into how human activity recognition models achieve their results. Now it is possible to analyze which features are important to a HAR system in each validation methodology in a simplified way. We not only demonstrate that the validation procedure k-folds cross-validation (k-CV), used in most works to evaluate the expected error in a HAR system, can overestimate by about 13% the prediction accuracy in three public datasets but also choose a different feature set when compared with the universal model. Combining explainable methods with machine learning algorithms has the potential to help new researchers look inside the decisions of the machine learning algorithms, avoiding most times the overestimation of prediction accuracy, understanding relations between features, and finding bias before deploying the system in real-world scenarios.

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6

Giri, Pranit. "Human Activity Recognition System." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 6671–73. http://dx.doi.org/10.22214/ijraset.2023.53135.

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Abstract: Almost every university has its management system to manage the students' records. Currently, even though there is a student management system that manages the students' records in Universiti Malaysia Sarawak (UNIMAS), no permission is provided for lecturers to access the system. This is because the access permission is only to top management such as Deans and Deputy Deans of Undergraduate and Student Development due to its privacy setting. Thus, this project proposes a system named Student Performance Analysis System (SPAS) to keep track of students' results in the Faculty of Computer Science and Information Technology (FCSIT). The proposed system offers a predictive system that can predict the student's performance in the course "TMC1013 System Analysis and Design", which in turn assists the lecturers from the Information System department to identify students that are predicted to have bad performance in the course "TMC1013 System Analysis and Design". The proposed system offers student performance prediction through the rules generated via the data mining technique. The data mining technique used in this project is classification, which classifies the students based on students' grades. Keywords- Student performance; student analysis; data mining; student performance analysis; classification; prediction; system

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7

Bhambri, Pankaj, Sachin Bagga, Dhanuka Priya, Harnoor Singh, and Harleen Kaur Dhiman. "Suspicious Human Activity Detection System." December 2020 2, no. 4 (October 31, 2020): 216–21. http://dx.doi.org/10.36548/jismac.2020.4.005.

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In collaboration with machine learning and artificial intelligence, anomaly detection systems are vastly used in behavioral analysis so that you can help in identity and prediction of prevalence of anomalies. It has applications in enterprise, from intrusion detection to system fitness tracking, and from fraud detection in credit score card transactions to fault detection in running environments. With the growing crime charges and human lack of confidence globally, majority of the countries are adopting precise anomaly detection systems to approach closer to a comfy area. Visualizing the Indian crime index which stands at 42. 38, the adoption of anomaly detection structures is an alarming want of time. Our own cannot be prevented with the aid of CCTV installations. These systems not simplest lead to identification on my own, but their optimized versions can help in prediction of unusual activities as properly.

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8

Xu-Nan Tan, Xu-Nan Tan. "Human Activity Recognition Based on CNN and LSTM." 電腦學刊 34, no. 3 (June 2023): 221–35. http://dx.doi.org/10.53106/199115992023063403016.

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<p>Human activity recognition (HAR) based on wearable devices is an emerging field of great interest. HAR can provide additional information on a human subject’s physical status. Utilising new technologies for HAR will become very meaningful with the development of deep learning. This study aims to mine deep learning models for HAR prediction with the highest accuracy on the basis of time-series data collected by mobile wearable devices. To this end, convolutional neural networks (CNN) and long short-term memory neural networks (LSTM) are combined in a deep network model to extract behavioural facts. The proposed CNN model contains two convolutional layers and a maximum pooling layer, and batch normalisation is added after each convolutional layer to improve convergence speed and avoid overfitting. This structure yields significant results in terms of performance. The model is evaluated on the MHEALTH dataset with a test set accuracy of 99.61% and can be used for the intelligent recognition of human activity. The results of this study show that the proposed model has better robustness and motion pattern detection capability compared to other models.</p> <p> </p>

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9

Esther, Ekemeyong, and Teresa Zielińska. "Predicting Human Activity – State of the Art." Pomiary Automatyka Robotyka 27, no. 2 (June 16, 2023): 31–46. http://dx.doi.org/10.14313/par_248/31.

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Predicting human actions is a very actual research field. Artificial intelligence methods are commonly used here. They enable early recognition and classification of human activities. Such knowledge is extremely needed in the work on robots and other interactive systems that communicate and cooperate with people. This ensures early reactions of such devices and proper planning of their future actions. However, due to the complexity of human actions, predicting them is a difficult task. In this article, we review state-of-the-art methods and summarize recent advances in predicting human activity. We focus in particular on four approaches using machine learning methods, namely methods using: artificial neural networks, support vector machines, probabilistic models and decision trees. We discuss the advantages and disadvantages of these approaches, as well as current challenges related to predicting human activity. In addition, we describe the types of sensors and data sets commonly used in research on predicting and recognizing human actions. We analyze the quality of the methods used, based on the prediction accuracy reported in scientific articles. We describe the importance of the data type and the parameters of machine learning models. Finally, we summarize the latest research trends. The article is intended to help in choosing the right method of predicting human activity, along with an indication of the tools and resources necessary to effectively achieve this goal.

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10

Liu, Zhenguang, Kedi Lyu, Shuang Wu, Haipeng Chen, Yanbin Hao, and Shouling Ji. "Aggregated Multi-GANs for Controlled 3D Human Motion Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2225–32. http://dx.doi.org/10.1609/aaai.v35i3.16321.

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Human motion prediction from historical pose sequence is at the core of many applications in machine intelligence. However, in current state-of-the-art methods, the predicted future motion is confined within the same activity. One can neither generate predictions that differ from the current activity, nor manipulate the body parts to explore various future possibilities. Undoubtedly, this greatly limits the usefulness and applicability of motion prediction. In this paper, we propose a generalization of the human motion prediction task in which control parameters can be readily incorporated to adjust the forecasted motion. Our method is compelling in that it enables manipulable motion prediction across activity types and allows customization of the human movement in a variety of fine-grained ways. To this aim, a simple yet effective composite GAN structure, consisting of local GANs for different body parts and aggregated via a global GAN is presented. The local GANs game in lower dimensions, while the global GAN adjusts in high dimensional space to avoid mode collapse. Extensive experiments show that our method outperforms state-of-the-art. The codes are available at https://github.com/herolvkd/AM-GAN.

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11

Tamaki, Toru, Tsubasa Hirakawa, Takayoshi Yamashita, and Hironobu Fujiyoshi. "Human Trajectory Analysis and Activity Prediction in Videos." Journal of the Robotics Society of Japan 35, no. 8 (2017): 610–15. http://dx.doi.org/10.7210/jrsj.35.610.

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12

Septiadi, Jaka, Budi Warsito, and Adi Wibowo. "Human Activity Prediction using Long Short Term Memory." E3S Web of Conferences 202 (2020): 15008. http://dx.doi.org/10.1051/e3sconf/202020215008.

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Early symptoms of dementia is one of the causes decrease in quality of life. Human activity recognition (HAR) system is proposed to recognize the daily routines which has an important role in detecting early symptoms of dementia. Long Short Term Memory (LSTM) is very useful for sequence analysis that can find the pattern of activities that carried out in daily routines. However, the LSTM model is slow to achieving convergence and take a long time during training. In this paper, we investigated the sequence of actions recorded in smart home sensors data using LSTM model, then the model will be optimized using several optimization methods. The optimization methods were Stochastic Gradient Descent (SGD), Adagrad, Adadelta, RMSProp, and Adam. The results showed that using Adam to optimized LSTM is better than other optimization methods.

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13

Al-juaifari, Mohammad Khalaf Rahim, and Alih Ali Athari. "Future Human Activity Prediction Using Wavelet And Lstm." Journal of Duhok University 26, no. 2 (December 21, 2023): 541–50. http://dx.doi.org/10.26682/csjuod.2023.26.2.49.

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14

Liu, Xiaoli, and Jianqin Yin. "Multi-Head TrajectoryCNN: A New Multi-Task Framework for Action Prediction." Applied Sciences 12, no. 11 (May 26, 2022): 5381. http://dx.doi.org/10.3390/app12115381.

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Action prediction is an important task in human activity analysis, which has many practical applications, such as human–robot interactions and autonomous driving. Action prediction often comprises two subtasks: action semantic prediction and future human motion prediction. Most of the existing works treat these subtasks separately, ignoring the correlations, leading to unsatisfying performance. By contrast, we jointly model these tasks and improve human motion predictions utilizing their action semantics. In terms of methodology, we propose a novel multi-task framework (Multi-head TrajectoryCNN) to simultaneously predict the action semantics and human motion of future human movements. Specifically, we first extract a general spatiotemporal representation of partial observations via two regression blocks. Then, we propose a regression head and a classification head for predicting future human motion and action semantics of human motion, respectively. For the regression head, another two stacked regression blocks and two convolutional layers are applied to predict future poses from the general representation learning. For the classification head, we propose a classification block and stack two regression blocks to predict action semantics from the general representation. In this way, the regression and classification heads are incorporated into a unified framework. During the backward propagation of the network, the human motion prediction and the semantic prediction may be enhanced by each other. NTU RGB+D is a widely used large-scale dataset for action recognition, which was collected by 40 different subjects from three views. Based on the official protocols, we use the skeletal modality and process action sequences with fixed lengths for the evaluation of our action prediction task. Experiments on NTU RGB+D show our model’s state-of-the-art performance. Furthermore, the experimental results also show that semantic information is of great help in predicting future human motion.

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15

Kumari, Sweta, Syed Shahid Raza, Gopal Arora, and Shambhu Bharadwaj. "Exploring machine learning in the context of environmental usage prediction." Multidisciplinary Science Journal 6 (July 26, 2024): 2024ss0503. http://dx.doi.org/10.31893/multiscience.2024ss0503.

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The use of environmental prediction refers to predicting the impact that human activity will have on ecosystems, natural resources and other environmental factors in the future. This strategy looks at historical patterns, present situations and future predictions to hypothesize about the ecological effects of human activities, climate change and other factors. This research suggests machine learning(ML) techniques to predict environmental uses. Prediction accuracy declinesover time and models face challenges due to the need for observable data integration in sectors like agriculture, energy and waterfor successful sub-seasonal predictions. To tackle these issues, we proposed a Next Generation Bumble Bee Mating Optimized Naïve Bayes Algorithm (NGBBMO-NBA) method that is used to enhance weather prediction. The research gathers the SSF dataset to make predictions on the usage of the environment. We use a min-max normalization approach for data preprocessing. The principalcomponent analysis (PCA) method extracts features from the SSF data. Environmental uncertainty inhibits sub-seasonal projections. Our suggested method, NGBBMO-NBA, surpasses the current techniques for ecological prediction in terms of energy consumption (96.5%), F1-Score (96%), Mean Absolute Error (MAE) (97) and Root Mean Square Error (RMSE) (98.5).

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16

Zanchettin, Andrea Maria, Andrea Casalino, Luigi Piroddi, and Paolo Rocco. "Prediction of Human Activity Patterns for Human–Robot Collaborative Assembly Tasks." IEEE Transactions on Industrial Informatics 15, no. 7 (July 2019): 3934–42. http://dx.doi.org/10.1109/tii.2018.2882741.

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17

Hmamouche, Youssef, Magalie Ochs, Laurent Prévot, and Thierry Chaminade. "Interpretable prediction of brain activity during conversations from multimodal behavioral signals." PLOS ONE 19, no. 3 (March 21, 2024): e0284342. http://dx.doi.org/10.1371/journal.pone.0284342.

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We present an analytical framework aimed at predicting the local brain activity in uncontrolled experimental conditions based on multimodal recordings of participants’ behavior, and its application to a corpus of participants having conversations with another human or a conversational humanoid robot. The framework consists in extracting high-level features from the raw behavioral recordings and applying a dynamic prediction of binarized fMRI-recorded local brain activity using these behavioral features. The objective is to identify behavioral features required for this prediction, and their relative weights, depending on the brain area under investigation and the experimental condition. In order to validate our framework, we use a corpus of uncontrolled conversations of participants with a human or a robotic agent, focusing on brain regions involved in speech processing, and more generally in social interactions. The framework not only predicts local brain activity significantly better than random, it also quantifies the weights of behavioral features required for this prediction, depending on the brain area under investigation and on the nature of the conversational partner. In the left Superior Temporal Sulcus, perceived speech is the most important behavioral feature for predicting brain activity, regardless of the agent, while several features, which differ between the human and robot interlocutors, contribute to the prediction in regions involved in social cognition, such as the TemporoParietal Junction. This framework therefore allows us to study how multiple behavioral signals from different modalities are integrated in individual brain regions during complex social interactions.

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18

Luo, Heng, Hao Ye, Hui Wen Ng, Lemming Shi, Weida Tong, Donna L. Mendrick, and Huixiao Hong. "Machine Learning Methods for Predicting HLA-Peptide Binding Activity." Bioinformatics and Biology Insights 9s3 (January 2015): BBI.S29466. http://dx.doi.org/10.4137/bbi.s29466.

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As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA-peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine learning methods and tools that have been used for HLA-peptide binding prediction. We also summarize the descriptors based on which the HLA-peptide binding prediction models have been constructed and discuss the limitation and challenges of the current methods. Lastly, we give a future perspective on the HLA-peptide binding prediction method based on network analysis.

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19

Park, Jinsoo, Chiyou Song, Mingi Kim, and Sungroul Kim. "Activity Prediction Based on Deep Learning Techniques." Applied Sciences 13, no. 9 (May 5, 2023): 5684. http://dx.doi.org/10.3390/app13095684.

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Studies on real-time PM2.5 concentrations per activity in microenvironments are gaining a lot of attention due to their considerable impact on health. These studies usually assume that information about human activity patterns in certain environments is known beforehand. However, if a person’s activity pattern can be inferred reversely using environmental information, it can be easier to access the levels of PM2.5 concentration that affect human health. This study collected the actual data necessary for this purpose and designed a deep learning algorithm that can infer human activity patterns reversely using the collected dataset. The dataset was collected based on a realistic scenario, which includes activity patterns in both indoor and outdoor environments. The deep learning models used include the well-known multilayer perception (MLP) model and a long short-term memory (LSTM) model. The performance of the designed deep learning algorithm was evaluated using training and test data. Simulation results showed that the LSTM model has a higher average test accuracy of more than 15% compared to the MLP model, and overall, we were able to achieve high accuracy of over 90% on average.

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20

De Bock, Yannick, Andres Auquilla, Ann Nowé, and Joost R. Duflou. "Nonparametric user activity modelling and prediction." User Modeling and User-Adapted Interaction 30, no. 5 (March 14, 2020): 803–31. http://dx.doi.org/10.1007/s11257-020-09259-3.

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21

Sairam, B. V. V. S. "Human Activity Pattern Prediction System for Smart Home Appliances." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 1811–14. http://dx.doi.org/10.22214/ijraset.2021.39628.

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Abstract: This paper proposes a model (HAPP) for learning and finding human action designs for Smart home applications based on huge amounts of data from smart homes. The proposed methodology quantifies and breaks down vitality use variations initiated by renters' behaviour using visit design mining, group research, and expectation. The HAPP System addresses the legal obligation to deconstruct energy consumption patterns at the machine level, which is directly linked to the actions of human. In the quantum/information cut of 24th, the information from shrewd meter is recursively mined, and the results are stored up throughout progressive mining works out. The HAPP System specifies the conditions for analysing the project that we use Keywords: Smart home, Data Mining, classifications, Human activity recognition

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22

Li, Kang, and Yun Fu. "Prediction of Human Activity by Discovering Temporal Sequence Patterns." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 8 (August 2014): 1644–57. http://dx.doi.org/10.1109/tpami.2013.2297321.

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23

Ding, Wenwen, Kai Liu, Fei Cheng, and Jin Zhang. "Learning hierarchical spatio-temporal pattern for human activity prediction." Journal of Visual Communication and Image Representation 35 (February 2016): 103–11. http://dx.doi.org/10.1016/j.jvcir.2015.12.006.

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24

Wang, Haoran, Wankou Yang, Chunfeng Yuan, Haibin Ling, and Weiming Hu. "Human activity prediction using temporally-weighted generalized time warping." Neurocomputing 225 (February 2017): 139–47. http://dx.doi.org/10.1016/j.neucom.2016.11.004.

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25

Kondor, Dániel, Sebastian Grauwin, Zsófia Kallus, István Gódor, Stanislav Sobolevsky, and Carlo Ratti. "Prediction limits of mobile phone activity modelling." Royal Society Open Science 4, no. 2 (February 2017): 160900. http://dx.doi.org/10.1098/rsos.160900.

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Thanks to their widespread usage, mobile devices have become one of the main sensors of human behaviour and digital traces left behind can be used as a proxy to study urban environments. Exploring the nature of the spatio-temporal patterns of mobile phone activity could thus be a crucial step towards understanding the full spectrum of human activities. Using 10 months of mobile phone records from Greater London resolved in both space and time, we investigate the regularity of human telecommunication activity on urban scales. We evaluate several options for decomposing activity timelines into typical and residual patterns, accounting for the strong periodic and seasonal components. We carry out our analysis on various spatial scales, showing that regularity increases as we look at aggregated activity in larger spatial units with more activity in them. We examine the statistical properties of the residuals and show that it can be explained by noise and specific outliers. Also, we look at sources of deviations from the general trends, which we find to be explainable based on knowledge of the city structure and places of attractions. We show examples how some of the outliers can be related to external factors such as specific social events.

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26

Jaramillo, Ismael Espinoza, Channabasava Chola, Jin-Gyun Jeong, Ji-Heon Oh, Hwanseok Jung, Jin-Hyuk Lee, Won Hee Lee, and Tae-Seong Kim. "Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks." Sensors 23, no. 14 (July 18, 2023): 6491. http://dx.doi.org/10.3390/s23146491.

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Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data (i.e., past information) recognizing human activities. In fact, HAR works based on future sensor data to predict human activities are rare. Human Activity Prediction (HAP) can benefit in multiple applications, such as fall detection or exercise routines, to prevent injuries. This work presents a novel HAP system based on forecasted activity data of Inertial Measurement Units (IMU). Our HAP system consists of a deep learning forecaster of IMU activity signals and a deep learning classifier to recognize future activities. Our deep learning forecaster model is based on a Sequence-to-Sequence structure with attention and positional encoding layers. Then, a pre-trained deep learning Bi-LSTM classifier is used to classify future activities based on the forecasted IMU data. We have tested our HAP system for five daily activities with two tri-axial IMU sensors. The forecasted signals show an average correlation of 91.6% to the actual measured signals of the five activities. The proposed HAP system achieves an average accuracy of 97.96% in predicting future activities.

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27

Shakerian, Ali, Victor Douet, Amirhossein Shoaraye Nejati, and René Landry. "Real-Time Sensor-Embedded Neural Network for Human Activity Recognition." Sensors 23, no. 19 (September 28, 2023): 8127. http://dx.doi.org/10.3390/s23198127.

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This article introduces a novel approach to human activity recognition (HAR) by presenting a sensor that utilizes a real-time embedded neural network. The sensor incorporates a low-cost microcontroller and an inertial measurement unit (IMU), which is affixed to the subject’s chest to capture their movements. Through the implementation of a convolutional neural network (CNN) on the microcontroller, the sensor is capable of detecting and predicting the wearer’s activities in real-time, eliminating the need for external processing devices. The article provides a comprehensive description of the sensor and the methodology employed to achieve real-time prediction of subject behaviors. Experimental results demonstrate the accuracy and high inference performance of the proposed solution for real-time embedded activity recognition.

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28

Reily, Brian, Fei Han, Lynne E. Parker, and Hao Zhang. "Skeleton-based bio-inspired human activity prediction for real-time human–robot interaction." Autonomous Robots 42, no. 6 (December 27, 2017): 1281–98. http://dx.doi.org/10.1007/s10514-017-9692-3.

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29

Salomón, Sergio, and Cristina Tîrnăucă. "Human Activity Recognition through Weighted Finite Automata." Proceedings 2, no. 19 (October 25, 2018): 1263. http://dx.doi.org/10.3390/proceedings2191263.

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This work addresses the problem of human activity identification in an ubiquitous environment, where data is collected from a wide variety of sources. In our approach, after filtering noisy sensor entries, we learn user’s behavioral patterns and activities’ sensor patterns through the construction of weighted finite automata and regular expressions respectively, and infer the inhabitant’s position for each activity through frequency distribution of floor sensor data. Finally, we analyze the prediction results of this strategy, which obtains 90.65% accuracy for the test data.

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30

Makkouk, Al H., Isaac B. Bersuker, and James E. Boggs. "Quantitative Drug Activity Prediction for Inhibitors of Human Breast Carcinoma." International Journal of Pharmaceutical Medicine 18, no. 2 (2004): 81–89. http://dx.doi.org/10.2165/00124363-200418020-00002.

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31

Yamada, Yohei, Katsuyuki Sakai, and Yukiyasu Kamitani. "Prediction of future perceptual alternation timing from human brain activity." Neuroscience Research 65 (January 2009): S133—S134. http://dx.doi.org/10.1016/j.neures.2009.09.653.

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32

Sharma, Divya, and Usha Chauhan. "Human Activity Prediction Studies Using Wearable Sensors and Machine Learning." Journal of Computer Science 20, no. 4 (April 1, 2024): 431–41. http://dx.doi.org/10.3844/jcssp.2024.431.441.

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33

Das, Shuvojit. "Human Activity Recognition using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 4188–93. http://dx.doi.org/10.22214/ijraset.2022.44722.

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Abstract: Nowadays, activity recognition is one of the most popular uses of machine learning algorithms. It's utilized in biomedical engineering, game production, and producing better metrics for sports training, among other things. Data from sensors linked to a person may be used to build supervised machine learning models that predict the activity that the person is doing. We will use data from the UCI Machine Learning Repository in this work. It contains data from the phone's accelerometer, gyroscope, and other sensors, which is used to build supervised prediction models using machine learning techniques like as SVM, Random Forest. This may be used to forecast the person's kind of movement, which is separated into six categories: walking, walking upstairs, walking downstairs, sitting, standing, and lying. We'll use a confusion matrix to compare the accuracy of different models.

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34

Ramos, Raúl Gómez, Jaime Duque Domingo, Eduardo Zalama, and Jaime Gómez-García-Bermejo. "Daily Human Activity Recognition Using Non-Intrusive Sensors." Sensors 21, no. 16 (August 4, 2021): 5270. http://dx.doi.org/10.3390/s21165270.

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In recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home. This work focuses on developing a system capable of recognising the most usual activities in the daily life of an elderly person in real-time to enable a specialist to monitor the habits of this person, such as taking medication or eating the correct meals of the day. To this end, a prediction model has been developed based on recurrent neural networks, specifically on bidirectional LSTM networks, to obtain in real-time the activity being carried out by the individuals in their homes, based on the information provided by a set of different sensors installed at each person’s home. The prediction model developed in this paper provides a 95.42% accuracy rate, improving the results of similar models currently in use. In order to obtain a reliable model with a high accuracy rate, a series of processing and filtering processes have been carried out on the data, such as a method based on a sliding window or a stacking and re-ordering algorithm, that are subsequently used to train the neural network, obtained from the public database CASAS.

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Xu, Li, and Mei‐Po Kwan. "Mining sequential activity–travel patterns for individual‐level human activity prediction using Bayesian networks." Transactions in GIS 24, no. 5 (May 30, 2020): 1341–58. http://dx.doi.org/10.1111/tgis.12635.

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Wang, Chia-Chi, Pinpin Lin, Che-Yu Chou, Shan-Shan Wang, and Chun-Wei Tung. "Prediction of human fetal–maternal blood concentration ratio of chemicals." PeerJ 8 (July 21, 2020): e9562. http://dx.doi.org/10.7717/peerj.9562.

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Background The measurement of human fetal-maternal blood concentration ratio (logFM) of chemicals is critical for the risk assessment of chemical-induced developmental toxicity. While a few in vitro and ex vivo experimental methods were developed for predicting logFM of chemicals, the obtained experimental results are not able to directly predict in vivo outcomes. Methods A total of 55 chemicals with logFM values representing in vivo fetal-maternal blood ratio were divided into training and test datasets. An interpretable linear regression model was developed along with feature selection methods. Cross-validation on training dataset and prediction on independent test dataset were conducted to validate the prediction model. Results This study presents the first valid quantitative structure-activity relationship model following the Organisation for Economic Co-operation and Development (OECD) guidelines based on multiple linear regression for predicting in vivo logFM values. The autocorrelation descriptor AATSC1c and information content descriptor ZMIC1 were identified as informative features for predicting logFM. After the adjustment of the applicability domain, the developed model performs well with correlation coefficients of 0.875, 0.850 and 0.847 for model fitting, leave-one-out cross-validation and independent test, respectively. The model is expected to be useful for assessing human transplacental exposure.

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Косяков, А. В., and А. Д. Ишков. "Neurophysiological bases for predicting the success of professional activity." Экономика и предпринимательство, no. 10(147) (February 21, 2023): 948–51. http://dx.doi.org/10.34925/eip.2022.147.10.188.

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Прогнозирование успешности профессиональной деятельности является одним из значимых элементов управленческой деятельности. Надежность прогнозов повышается, если известны внутренние механизмы исследуемых процессов. Точность прогнозов поведения человека может быть повышена при переходе на высший (типологический) уровень прогнозирования. Авторы предлагают использовать в качестве типологических оснований нейрофизиологические особенности человеческого организма: четыре взаимосвязанные пары нервных систем, обеспечивающие функционирование человека. Forecasting the success of professional activity is one of the significant elements of managerial activity. The reliability of forecasts increases if the internal mechanisms of the processes under study are known. The accuracy of predictions of human behavior can be increased by moving to the highest (typological) level of prediction. The authors propose to use the neurophysiological features of the human body as typological bases: four interconnected pairs of nervous systems that ensure the functioning of a person.

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Mogk, Jeremy P. M., and Peter J. Keir. "Prediction of forearm muscle activity during gripping." Ergonomics 49, no. 11 (September 15, 2006): 1121–30. http://dx.doi.org/10.1080/00140130600777433.

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Ramakrishnan, R., and P. Angarika. "SMART WATCH DATA ANALYSIS USING PYTHON AND HUMAN HEALTH PREDICTION." International Scientific Journal of Engineering and Management 03, no. 12 (December 14, 2024): 1–5. https://doi.org/10.55041/isjem02154.

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This project leverages smartwatch fitness data to predict health patterns and monitor daily activity trends, underscoring the role of wearables in personal health management. Using Python, Pandas, and Plotly, it handles data preprocessing, visualization, and predictive analysis on metrics such as step counts, calories burned, and active minutes. Data preprocessing includes managing missing values and standardizing the "Activity Date" field. Descriptive statistics and visualizations, including scatter plots, pie charts, and bar charts, uncover trends and behavioral patterns. Descriptive statistics provide insight into data distribution, while visualizations reveal significant trends. Scatter plots highlight correlations, such as between calories burned and steps taken, pie charts depict activity time allocation, and bar charts present active minutes across different days. These visualizations uncover behavioral patterns and emphasize data-driven insights. For predictive analysis, a Random Forest model is applied to forecast "very active minutes," representing high-intensity activity. Key predictive features include steps and calories burned, which strongly correlate with active minutes. The model achieved an accuracy of 80%, and validation metrics, such as MSE and R2, confirmed its reliability. This predictive capability offers users actionable insights for fitness improvement, helping them set realistic goals and monitor progress effectively. In conclusion, this study illustrates the practical applications of machine learning in wearable data analysis, showing potential for integration into fitness-tracking apps. The model’s insights support both short-term fitness and long- term health, with future improvements including additional metrics, like heart rate and sleep data, for comprehensive health monitoring. KEYWORDS: Smartwatch data analysis, very active minutes, Physical activity prediction, Random Forest algorithm, Personalized fitness monitoring, High-intensity activity, Machine learning in health, Predictive modeling.

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Ning, Lixin, Changxiu Cheng, Xu Lu, Shi Shen, Liang Zhang, Shaomin Mu, and Yunsheng Song. "Improving the Prediction of Soil Organic Matter in Arable Land Using Human Activity Factors." Water 14, no. 10 (May 23, 2022): 1668. http://dx.doi.org/10.3390/w14101668.

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Detailed spatial distribution of soil organic matter (SOM) in arable land is essential for agricultural management and decision making. Based on digital soil mapping (DSM) theory, much attention has been focused on the selection of environmental covariates. However, the importance of human activity factors in SOM prediction has not received enough attention, especially in arable soil. Moreover, due to the insufficient amount of soil sampling data used to train and validate the DSM model, the prediction results may be questionable, and some even contradictory. This paper explores the effectiveness of the human footprint, amount of fertilizer application, agronomic management level, crop planting type, and irrigation guarantee degree in SOM mapping of arable land in Heilongjiang Province. The results show that the model only including environmental covariates accounts for 41% of the variation in SOM distribution. The model combining the five human activity factors increases the SOM spatial prediction by 39% in terms of R2 (coefficient of determination), 12% in terms of RMSE (root mean square error), 15% in terms of MAE (mean absolute error), and 11% in terms of LCCC (Lin’s concordance correlation coefficient), showing better prediction accuracy and performance. This indicates that human activity factors play a crucial role in determining SOM distribution in arable land. In the SOM prediction, soil moisture is the most important environmental covariate, and the amount of fertilizer application with a relative importance of 11.36% (ranking 3rd) is the most important human activity factor, higher than the annual average precipitation and elevation. From a spatial point of view, the Sanjiang Plain is a difficult area for prediction.

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Kumar, Kambala Vijaya, and Jonnadula Harikiran. "Privacy preserving human activity recognition framework using an optimized prediction algorithm." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (March 1, 2022): 254. http://dx.doi.org/10.11591/ijai.v11.i1.pp254-264.

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Human activity recognition, in computer vision research, is the area of growing interest as it has plethora of real-world applications. Inferring actions from one or more persons captured through a live video has its immense utility in the contemporary era. Same time, protecting privacy of humans is to be given paramount importance. Many researchers contributed towards this end leading to privacy preserving action recognition systems. However, having an optimized model that can withstand any adversary models that strives to disclose privacy information. To address this problem, we proposed an algorithm known optimized prediction algorithm for privacy preserving activity recognition (OPA-PPAR) based on deep neural networks. It anonymizes video content to have adaptive privacy model that defeats attacks from adversaries. The privacy model enhances the privacy of humans while permitting highly accurate approach towards action recognition. The algorithm is implemented to realize privacy preserving human activity recognition framework (PPHARF). The visual recognition of human actions is made using an underlying adversarial learning process where the anonymization is optimized to have an adaptive privacy model. A dataset named human metabolome database (HMDB51) is used for empirical study. Our experiments with using Python data science platform reveal that the OPA-PPAR outperforms existing methods.

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Pagnoni, Giuseppe, Caroline F. Zink, P. Read Montague, and Gregory S. Berns. "Activity in human ventral striatum locked to errors of reward prediction." Nature Neuroscience 5, no. 2 (January 22, 2002): 97–98. http://dx.doi.org/10.1038/nn802.

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Zafar, Raheel, Sarat C. Dass, Aamir Saeed Malik, Nidal Kamel, M. Javvad Ur Rehman, Rana Fayyaz Ahmad, Jafri Malin Abdullah, and Faruque Reza. "Prediction of Human Brain Activity Using Likelihood Ratio Based Score Fusion." IEEE Access 5 (2017): 13010–19. http://dx.doi.org/10.1109/access.2017.2698068.

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Wang, Haoran, Chunfeng Yuan, Jifeng Shen, Wankou Yang, and Haibin Ling. "Action unit detection and key frame selection for human activity prediction." Neurocomputing 318 (November 2018): 109–19. http://dx.doi.org/10.1016/j.neucom.2018.08.037.

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Sun, Qianru, Hong Liu, Mengyuan Liu, and Tianwei Zhang. "Human activity prediction by mapping grouplets to recurrent Self-Organizing Map." Neurocomputing 177 (February 2016): 427–40. http://dx.doi.org/10.1016/j.neucom.2015.11.061.

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Wang, Lei, Xu Zhao, Yunfei Si, Liangliang Cao, and Yuncai Liu. "Context-Associative Hierarchical Memory Model for Human Activity Recognition and Prediction." IEEE Transactions on Multimedia 19, no. 3 (March 2017): 646–59. http://dx.doi.org/10.1109/tmm.2016.2617079.

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Noor, Shaheena, and Vali Uddin. "First Person Vision for Activity Prediction Using Probabilistic Modeling." October 2018 37, no. 4 (October 1, 2018): 545–58. http://dx.doi.org/10.22581/muet1982.1804.09.

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Identifying activities of daily living is an important area of research with applications in smart-homes and healthcare for elderly people. It is challenging due to reasons like human self-occlusion, complex natural environment and the human behavior when performing a complicated task. From psychological studies, we know that human gaze is closely linked with the thought process and we tend to “look” at the objects before acting on them. Hence, we have used the object information present in gaze images as the context and formed the basis for activity prediction. Our system is based on HMM (Hidden Markov Models) and trained using ANN (Artificial Neural Network). We begin with extracting motion information from TPV (Third Person Vision) streams and object information from FPV (First Person Vision) cameras. The advantage of having FPV is that the object information forms the context of the scene. When context is included as input to the HMM for activity recognition, the precision increases. For testing, we used two standard datasets from TUM (Technische Universitaet Muenchen) and GTEA Gaze+ (Georgia Tech Egocentric Activities). In the first round, we trained our ANNs only with activity information and in the second round added the object information as well. We saw a significant increase in the precision (and accuracy) of predicted activities from 55.21% (respectively 85.25%) to 77.61% (respectively 93.5%). This confirmed our initial hypothesis that including the focus of attention of the actor in the form of object seen in FPV can help in predicting activities better.

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Ramnani, N., I. Toni, O. Josephs, J. Ashburner, and R. E. Passingham. "Learning- and Expectation-Related Changes in the Human Brain During Motor Learning." Journal of Neurophysiology 84, no. 6 (December 1, 2000): 3026–35. http://dx.doi.org/10.1152/jn.2000.84.6.3026.

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We have studied a simple form of motor learning in the human brain so as to isolate activity related to motor learning and the prediction of sensory events. Whole-brain, event-related functional magnetic resonance imaging (fMRI) was used to record activity during classical discriminative delay eyeblink conditioning. Auditory conditioned stimulus (CS+) trials were presented either with a corneal airpuff unconditioned stimulus (US, paired), or without a US (unpaired). Auditory CS− trials were never reinforced with a US. Trials were presented pseudorandomly, 66 times each. The subjects gradually produced conditioned responses to CS+ trials, while increasingly differentiating between CS+ and CS− trials. The increasing difference between hemodynamic responses for unpaired CS+ and for CS− trials evolved slowly during conditioning in the ipsilateral cerebellar cortex (Crus I/Lobule HVI), contralateral motor cortex and hippocampus. To localize changes that were related to sensory prediction, we compared trials on which the expected airpuff US failed to occur (Unpaired CS+) with trials on which it occurred as expected (Paired CS+). Error-related signals in the contralateral cerebellum and somatosensory cortex were seen to increase during learning as the sensory prediction became stronger. The changes seen in the ipsilateral cerebellar cortex may be due either to the violations of sensory predictions, or to learning-related increases in the excitability of cerebellar neurons to presentations of the CS+.

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Breska, Assaf, and Richard B. Ivry. "Context-specific control over the neural dynamics of temporal attention by the human cerebellum." Science Advances 6, no. 49 (December 2020): eabb1141. http://dx.doi.org/10.1126/sciadv.abb1141.

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Physiological methods have identified a number of signatures of temporal prediction, a core component of attention. While the underlying neural dynamics have been linked to activity within cortico-striatal networks, recent work has shown that the behavioral benefits of temporal prediction rely on the cerebellum. Here, we examine the involvement of the human cerebellum in the generation and/or temporal adjustment of anticipatory neural dynamics, measuring scalp electroencephalography in individuals with cerebellar degeneration. When the temporal prediction relied on an interval representation, duration-dependent adjustments were impaired in the cerebellar group compared to matched controls. This impairment was evident in ramping activity, beta-band power, and phase locking of delta-band activity. These same neural adjustments were preserved when the prediction relied on a rhythmic stream. Thus, the cerebellum has a context-specific causal role in the adjustment of anticipatory neural dynamics of temporal prediction, providing the requisite modulation to optimize behavior.

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Singh, Suruchi, Dr C. S. Raghuvanshi, and Dr Hari Om Sharan. "Advancements and Future Directions in Human Activity Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 4097–102. http://dx.doi.org/10.22214/ijraset.2023.54400.

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Abstract: Activity recognition deals with the automation of recognizing various activities by identifying the subject and its interactions with the environment. Human activity recognition deals with identifying different activities like sitting, walking, laying moving, running, jogging, various hand movements, posture and behavior prediction and recognition. Activity recognition deals with the automation of recognizing various activities by identifying the subject and its interactions with the environment. The wide variety of environments are incorporated with Human Activity Recognition. It assists with the vast variety of problems related to people, living, lifestyle, security, monitoring, which may or may not be related to computer science.

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