Zeitschriftenartikel zum Thema „Detection and recognition of activities of daily living“
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Refonaa, J., Bandaru Suhas, B. V. S. Bhaskar, S. L. JanyShabu, S. Dhamodaran, Sardar Maran, Maria Anu und M. Lakshmi. „Fall Detection and Daily Living Activity Recognition Logic Regression“. Journal of Computational and Theoretical Nanoscience 17, Nr. 8 (01.08.2020): 3520–25. http://dx.doi.org/10.1166/jctn.2020.9223.
Der volle Inhalt der QuelleBelmonte-Fernández, Óscar, Antonio Caballer-Miedes, Eris Chinellato, Raúl Montoliu, Emilio Sansano-Sansano und Rubén García-Vidal. „Anomaly Detection in Activities of Daily Living with Linear Drift“. Cognitive Computation 12, Nr. 6 (01.07.2020): 1233–51. http://dx.doi.org/10.1007/s12559-020-09740-6.
Der volle Inhalt der QuelleHowedi, Aadel, Ahmad Lotfi und Amir Pourabdollah. „Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living“. Entropy 21, Nr. 4 (19.04.2019): 416. http://dx.doi.org/10.3390/e21040416.
Der volle Inhalt der QuelleMaunder, David, Julien Epps, Eliathamby Ambikairajah und Branko Celler. „Robust Sounds of Activities of Daily Living Classification in Two-Channel Audio-Based Telemonitoring“. International Journal of Telemedicine and Applications 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/696813.
Der volle Inhalt der QuelleIseda, Hikoto, Keiichi Yasumoto, Akira Uchiyama und Teruo Higashino. „Daily Living Activity Recognition with Frequency-Shift WiFi Backscatter Tags“. Sensors 24, Nr. 11 (21.05.2024): 3277. http://dx.doi.org/10.3390/s24113277.
Der volle Inhalt der QuellePires, Ivan Miguel, Gonçalo Marques, Nuno M. Garcia, Nuno Pombo, Francisco Flórez-Revuelta, Susanna Spinsante, Maria Canavarro Teixeira und Eftim Zdravevski. „Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices“. Electronics 8, Nr. 12 (07.12.2019): 1499. http://dx.doi.org/10.3390/electronics8121499.
Der volle Inhalt der QuelleJaveed, Madiha, Naif Al Mudawi, Abdulwahab Alazeb, Sultan Almakdi, Saud S. Alotaibi, Samia Allaoua Chelloug und Ahmad Jalal. „Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework“. Sensors 23, Nr. 18 (16.09.2023): 7927. http://dx.doi.org/10.3390/s23187927.
Der volle Inhalt der QuelleLee, Cheolhwan, Ah Hyun Yuh und Soon Ju Kang. „Real-Time Prediction of Resident ADL Using Edge-Based Time-Series Ambient Sound Recognition“. Sensors 24, Nr. 19 (04.10.2024): 6435. http://dx.doi.org/10.3390/s24196435.
Der volle Inhalt der QuelleBhattacharya, Sarnab, Rebecca Adaimi und Edison Thomaz. „Leveraging Sound and Wrist Motion to Detect Activities of Daily Living with Commodity Smartwatches“. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, Nr. 2 (04.07.2022): 1–28. http://dx.doi.org/10.1145/3534582.
Der volle Inhalt der QuelleHaghi, Mostafa, Arman Ershadi und Thomas M. Deserno. „Recognizing Human Activity of Daily Living Using a Flexible Wearable for 3D Spine Pose Tracking“. Sensors 23, Nr. 4 (12.02.2023): 2066. http://dx.doi.org/10.3390/s23042066.
Der volle Inhalt der QuelleSiong Jun, Sai, Hafiz Rashidi Ramli, Azura Che Soh, Noor Ain Kamsani, Raja Kamil Raja Ahmad, Siti Anom Ahmad und Asnor Juraiza Ishak. „Development of fall detection and activity recognition using threshold based method and neural network“. Indonesian Journal of Electrical Engineering and Computer Science 17, Nr. 3 (01.03.2020): 1338. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1338-1347.
Der volle Inhalt der QuelleQuero, Javier, Claire Orr, Shuai Zang, Chris Nugent, Alberto Salguero und Macarena Espinilla. „Real-time Recognition of Interleaved Activities Based on Ensemble Classifier of Long Short-Term Memory with Fuzzy Temporal Windows“. Proceedings 2, Nr. 19 (26.10.2018): 1225. http://dx.doi.org/10.3390/proceedings2191225.
Der volle Inhalt der QuelleSenyurek, Volkan, Masudul Imtiaz, Prajakta Belsare, Stephen Tiffany und Edward Sazonov. „Electromyogram in Cigarette Smoking Activity Recognition“. Signals 2, Nr. 1 (09.02.2021): 87–97. http://dx.doi.org/10.3390/signals2010008.
Der volle Inhalt der QuelleSyed, Abbas Shah, Daniel Sierra-Sosa, Anup Kumar und Adel Elmaghraby. „A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition“. Sensors 22, Nr. 7 (26.03.2022): 2547. http://dx.doi.org/10.3390/s22072547.
Der volle Inhalt der QuelleSaeed, Umer, Syed Yaseen Shah, Syed Aziz Shah, Jawad Ahmad, Abdullah Alhumaidi Alotaibi, Turke Althobaiti, Naeem Ramzan, Akram Alomainy und Qammer H. Abbasi. „Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living“. Electronics 10, Nr. 18 (12.09.2021): 2237. http://dx.doi.org/10.3390/electronics10182237.
Der volle Inhalt der QuelleZaoui, Chaimae, Faouzia Benabbou, Abdelaziz Ettaoufik und Khadija Sabiri. „Human Activity Recognition Using Convolutional Autoencoder and Advanced Preprocessing“. International Journal of Online and Biomedical Engineering (iJOE) 20, Nr. 04 (04.03.2024): 144–59. http://dx.doi.org/10.3991/ijoe.v20i04.43623.
Der volle Inhalt der QuelleDedabrishvili, Mariam, Natia Mamaiashvili und Ioseb Matiashvili. „Fall Detection System based on iOS Smartphone Sensors“. Journal of Technical Science and Technologies 8, Nr. 1 (30.04.2024): 35–44. http://dx.doi.org/10.31578/jtst.v8i1.153.
Der volle Inhalt der QuelleGayathri, K. S., K. S. Easwarakumar und Susan Elias. „Fuzzy Ontology Based Activity Recognition for Assistive Health Care Using Smart Home“. International Journal of Intelligent Information Technologies 16, Nr. 1 (Januar 2020): 17–31. http://dx.doi.org/10.4018/ijiit.2020010102.
Der volle Inhalt der QuelleNarkhede, Arsh, Hayden Gowing, Tod Vandenberg, Steven Phan, Jason Wong und Andrew Chan. „Automated Detection of In-Home Activities with Ultra-Wideband Sensors“. Sensors 24, Nr. 14 (20.07.2024): 4706. http://dx.doi.org/10.3390/s24144706.
Der volle Inhalt der QuelleGhayvat, Hemant, Muhammad Awais, Sharnil Pandya, Hao Ren, Saeed Akbarzadeh, Subhas Chandra Mukhopadhyay, Chen Chen, Prosanta Gope, Arpita Chouhan und Wei Chen. „Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection“. Sensors 19, Nr. 4 (13.02.2019): 766. http://dx.doi.org/10.3390/s19040766.
Der volle Inhalt der QuelleWu, Jiaxuan, Yunfei Feng und Carl K. Chang. „Sound of Daily Living Identification Based on Hierarchical Situation Audition“. Sensors 23, Nr. 7 (04.04.2023): 3726. http://dx.doi.org/10.3390/s23073726.
Der volle Inhalt der QuelleVavoulas, George, Matthew Pediaditis, Charikleia Chatzaki, Emmanouil G. Spanakis und Manolis Tsiknakis. „The MobiFall Dataset“. International Journal of Monitoring and Surveillance Technologies Research 2, Nr. 1 (Januar 2014): 44–56. http://dx.doi.org/10.4018/ijmstr.2014010103.
Der volle Inhalt der QuelleDiete, Alexander, und Heiner Stuckenschmidt. „Fusing Object Information and Inertial Data for Activity Recognition“. Sensors 19, Nr. 19 (23.09.2019): 4119. http://dx.doi.org/10.3390/s19194119.
Der volle Inhalt der QuelleXefteris, S., N. Doulamis, V. Andronikou, T. Varvarigou und G. Cambourakis. „Behavioral Biometrics in Assisted Living: A Methodology for Emotion Recognition“. Engineering, Technology & Applied Science Research 6, Nr. 4 (26.08.2016): 1035–44. http://dx.doi.org/10.48084/etasr.634.
Der volle Inhalt der QuelleNegrete Ramírez, José Manuel, Philippe Roose, Marc Dalmau, Yudith Cardinale und Edgar Silva. „A DSL-Based Approach for Detecting Activities of Daily Living by Means of the AGGIR Variables“. Sensors 21, Nr. 16 (23.08.2021): 5674. http://dx.doi.org/10.3390/s21165674.
Der volle Inhalt der QuelleLopez-Nava, Irvin Hussein, Matias Garcia-Constantino und Jesus Favela. „Recognition of Gait Activities Using Acceleration Data from A Smartphone and A Wearable Device“. Proceedings 31, Nr. 1 (21.11.2019): 60. http://dx.doi.org/10.3390/proceedings2019031060.
Der volle Inhalt der QuelleSeyedkazemi Ardebili, E., S. Eken und K. Küçük. „ACTIVITY RECOGNITION FOR AMBIENT SENSING DATA AND RULE BASED ANOMALY DETECTION“. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W3-2020 (23.11.2020): 379–82. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w3-2020-379-2020.
Der volle Inhalt der QuelleKańtoch, Eliasz. „Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk“. Sensors 18, Nr. 10 (24.09.2018): 3219. http://dx.doi.org/10.3390/s18103219.
Der volle Inhalt der QuelleZhang, Yiyuan, Ine D’Haeseleer, José Coelho, Vero Vanden Abeele und Bart Vanrumste. „Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations“. Sensors 21, Nr. 6 (20.03.2021): 2176. http://dx.doi.org/10.3390/s21062176.
Der volle Inhalt der QuelleKarakostas, Anastasios, Alexandra König, Carlos Fernando Crispim-Junior, François Bremond, Alexandre Derreumaux, Ioulietta Lazarou, Ioannis Kompatsiaris, Magda Tsolaki und Philippe Robert. „A French–Greek Cross-Site Comparison Study of the Use of Automatic Video Analyses for the Assessment of Autonomy in Dementia Patients“. Biosensors 10, Nr. 9 (21.08.2020): 103. http://dx.doi.org/10.3390/bios10090103.
Der volle Inhalt der QuelleYan, Jianjun, Xueqiang Wang, Jiangtao Shi und Shuai Hu. „Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks“. Sensors 23, Nr. 4 (14.02.2023): 2153. http://dx.doi.org/10.3390/s23042153.
Der volle Inhalt der QuelleDavis, Jensen, Shannon Howard, Gregory King, Phanidar Boddu, Kiran Jyothi und Joan McDowd. „ALEXA, ASSESS MY MEMORY: THE FEASIBILITY OF EXTENDED HEALTH MONITORING IN AN OLDER-ADULT-LIVING COMMUNITY“. Innovation in Aging 3, Supplement_1 (November 2019): S337. http://dx.doi.org/10.1093/geroni/igz038.1224.
Der volle Inhalt der QuelleArshad, Muhammad Haseeb, Muhammad Bilal und Abdullah Gani. „Human Activity Recognition: Review, Taxonomy and Open Challenges“. Sensors 22, Nr. 17 (27.08.2022): 6463. http://dx.doi.org/10.3390/s22176463.
Der volle Inhalt der QuellePapadogiorgaki, Maria, Nikos Grammalidis, Athina Grammatikopoulou, Konstantinos Apostolidis, Ekaterini S. Bei, Kostas Grigoriadis, Stylianos Zafeiris, George Livanos, Vasileios Mezaris und Michalis E. Zervakis. „An Integrated Support System for People with Intellectual Disability“. Electronics 12, Nr. 18 (08.09.2023): 3803. http://dx.doi.org/10.3390/electronics12183803.
Der volle Inhalt der QuelleQiu, Yuting, James Meng und Baihua Li*. „Automated Falls Detection Using Visual Anomaly Detection and Pose-based Approaches: Experimental Review and Evaluation“. Journal of Biomedical Research & Environmental Sciences 5, Nr. 1 (Januar 2024): 055–63. http://dx.doi.org/10.37871/jbres1872.
Der volle Inhalt der QuelleCondado, Paulo A., und Fernando G. Lobo. „Security and privacy concerns in assisted living environments“. Journal of Smart Cities and Society 2, Nr. 2 (23.08.2023): 99–121. http://dx.doi.org/10.3233/scs-230015.
Der volle Inhalt der QuelleSaeed, Aaqib, Tanir Ozcelebi und Johan Lukkien. „Synthesizing and Reconstructing Missing Sensory Modalities in Behavioral Context Recognition“. Sensors 18, Nr. 9 (06.09.2018): 2967. http://dx.doi.org/10.3390/s18092967.
Der volle Inhalt der QuelleOrtíz-Barrios, Miguel Angel, Ian Cleland, Chris Nugent, Pablo Pancardo, Eric Järpe und Jonathan Synnott. „Simulated Data to Estimate Real Sensor Events—A Poisson-Regression-Based Modelling“. Remote Sensing 12, Nr. 5 (28.02.2020): 771. http://dx.doi.org/10.3390/rs12050771.
Der volle Inhalt der QuelleAkbari, Ali, Jonathan Martinez und Roozbeh Jafari. „Facilitating Human Activity Data Annotation via Context-Aware Change Detection on Smartwatches“. ACM Transactions on Embedded Computing Systems 20, Nr. 2 (März 2021): 1–20. http://dx.doi.org/10.1145/3431503.
Der volle Inhalt der QuelleKarvonen, Niklas, und Denis Kleyko. „A Domain Knowledge-Based Solution for Human Activity Recognition: The UJA Dataset Analysis“. Proceedings 2, Nr. 19 (19.10.2018): 1261. http://dx.doi.org/10.3390/proceedings2191261.
Der volle Inhalt der QuelleLeon, Beatriz, Angelo Basteris, Francesco Infarinato, Patrizio Sale, Sharon Nijenhuis, Gerdienke Prange und Farshid Amirabdollahian. „Grasps Recognition and Evaluation of Stroke Patients for Supporting Rehabilitation Therapy“. BioMed Research International 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/318016.
Der volle Inhalt der QuelleHan, Kun, Qiongqian Yang und Zefan Huang. „A Two-Stage Fall Recognition Algorithm Based on Human Posture Features“. Sensors 20, Nr. 23 (05.12.2020): 6966. http://dx.doi.org/10.3390/s20236966.
Der volle Inhalt der QuelleCasilari, Eduardo, Moisés Álvarez-Marco und Francisco García-Lagos. „A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems“. Symmetry 12, Nr. 4 (20.04.2020): 649. http://dx.doi.org/10.3390/sym12040649.
Der volle Inhalt der QuellePrabhakar, Ashish John, Srikanth Prabhu, Aayush Agrawal, Siddhisa Banerjee, Abraham M. Joshua, Yogeesh Dattakumar Kamat, Gopal Nath und Saptarshi Sengupta. „Use of Machine Learning for Early Detection of Knee Osteoarthritis and Quantifying Effectiveness of Treatment Using Force Platform“. Journal of Sensor and Actuator Networks 11, Nr. 3 (23.08.2022): 48. http://dx.doi.org/10.3390/jsan11030048.
Der volle Inhalt der QuelleMihoub, Alaeddine. „A Deep Learning-Based Framework for Human Activity Recognition in Smart Homes“. Mobile Information Systems 2021 (11.09.2021): 1–11. http://dx.doi.org/10.1155/2021/6961343.
Der volle Inhalt der QuelleShahid, Zahraa Khais, Saguna Saguna und Christer Åhlund. „Detecting Anomalies in Daily Activity Routines of Older Persons in Single Resident Smart Homes: Proof-of-Concept Study“. JMIR Aging 5, Nr. 2 (11.04.2022): e28260. http://dx.doi.org/10.2196/28260.
Der volle Inhalt der QuelleMadokoro, Hirokazu, Stephanie Nix, Hanwool Woo und Kazuhito Sato. „A Mini-Survey and Feasibility Study of Deep-Learning-Based Human Activity Recognition from Slight Feature Signals Obtained Using Privacy-Aware Environmental Sensors“. Applied Sciences 11, Nr. 24 (12.12.2021): 11807. http://dx.doi.org/10.3390/app112411807.
Der volle Inhalt der QuelleQadir, Muhammad Usman, Izhar Ul Haq, Muhammad Awais Khan, Kamran Shah, Houssam Chouikhi und Mohamed A. Ismail. „Design, Analysis, and Development of Low-Cost State-of-the-Art Magnetorheological-Based Microprocessor Prosthetic Knee“. Sensors 24, Nr. 1 (01.01.2024): 255. http://dx.doi.org/10.3390/s24010255.
Der volle Inhalt der QuelleWu, Jiaxuan, Yunfei Feng und Peng Sun. „Sensor Fusion for Recognition of Activities of Daily Living“. Sensors 18, Nr. 11 (19.11.2018): 4029. http://dx.doi.org/10.3390/s18114029.
Der volle Inhalt der QuelleIhianle, Isibor Kennedy, Usman Naeem und Abdel-Rahman Tawil. „Recognition of Activities of Daily Living from Topic Model“. Procedia Computer Science 98 (2016): 24–31. http://dx.doi.org/10.1016/j.procs.2016.09.007.
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