Smart City Gnosys
Smart city article details
| Title | Comparative Analysis Of Different Approaches To Human Activity Recognition Based On Accelerometer Signals |
|---|---|
| ID_Doc | 14988 |
| Authors | Gomaa W. |
| Year | 2021 |
| Published | Studies in Big Data, 77 |
| DOI | http://dx.doi.org/10.1007/978-3-030-59338-4_16 |
| Abstract | Recently, automatic human activity recognition has drawn much attention. On one hand, this is due to the rapid proliferation and cost degradation of a wide variety of sensing hardware. On the other hand there are urgent growing and pressing demands from many application domains such as: in-home health monitoring especially for the elderly, smart cities, safe driving by monitoring and predicting driver’s behavior, healthcare applications, entertainment, assessment of therapy, performance evaluation in sports, etc. In this paper we focus on activities of daily living (ADL), which are routine activities that people tend to do every day without needing assistance. We have used a public dataset of acceleration data collected with a wrist-worn accelerometer for 14 different ADL activities. Our objective is to perform an extensive comparative study of the predictive power of several paradigms to model and classify ADL activities. To the best of our knowledge, almost all techniques for activity recognition are based on methods from the machine learning literature (particularly, supervised learning). Our comparative study widens the scope of techniques that can be used for automatic analysis of human activities and provides a valuation of the relative effectiveness and efficiency of a potentially myriad pool of techniques. We apply two different paradigms for the analysis and classification of daily living activities: (1) techniques based on supervised machine learning and (2) techniques based on estimating the empirical distribution of the time series data and use metric-theoretic techniques to estimate the dissimilarity between two distributions. We used several evaluation metrics including confusion matrices, overall accuracy, sensitivity and specificity for each activity, and relative computational performance. In each approach we used some of the well-known techniques in our experimentation and analysis. For example, in supervised learning we applied both support vector machines and random forests. One of the main conclusions from our analysis is that the simplest techniques, for example, using empirical discrete distributions as models, have almost the best performance in terms of both accuracy and computational efficiency. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Author Keywords | Accelerometer; Activities of daily living; Dissimilarity measure; Empirical probability distribution; Human activity recognition; Supervised learning; Time series; Wearable devices |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
3893  | 0.907 | Cruciani F.; Sun C.; Zhang S.; Nugent C.; Li C.; Song S.; Cheng C.; Cleland I.; McCullagh P. | A Public Domain Dataset For Human Activity Recognition In Free-Living Conditions | Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019 (2019) |
| 59700 | 0.901 | Sivakumar K.; Perumal T.; Yaakob R.; Marlisah E. | Unobstructive Human Activity Recognition: Probabilistic Feature Extraction With Optimized Convolutional Neural Network For Classification | AIP Conference Proceedings, 2816, 1 (2024) |
| 29569 | 0.886 | Chen L.; Nugent C.D. | Human Activity Recognition And Behaviour Analysis: For Cyber-Physical Systems In Smart Environments | Human Activity Recognition and Behaviour Analysis: For Cyber-Physical Systems in Smart Environments (2019) |
| 38615 | 0.882 | Kalimuthu S.; Perumal T.; Yaakob R.; Marlisah E.; Raghavan S. | Multiple Human Activity Recognition Using Iot Sensors And Machine Learning In Device-Free Environment: Feature Extraction, Classification, And Challenges: A Comprehensive Review | AIP Conference Proceedings, 2816, 1 (2024) |
| 17990 | 0.868 | Mizuno M.; Hasegawa T. | Deep Metric Learning For Sensor-Based Human Activity Recognition | ACM International Conference Proceeding Series (2019) |
| 44371 | 0.866 | Maaloul K.; Brahim L.; Abdelhamid N.M. | Real-Time Human Activity Recognition From Smart Phone Using Linear Support Vector Machines | Telkomnika (Telecommunication Computing Electronics and Control), 21, 3 (2023) |
| 6138 | 0.863 | Baghezza R.; Bouchard K.; Bouzouane A.; Gouin-Vallerand C. | Activity Recognition In The City Using Embedded Systems And Anonymous Sensors | Procedia Computer Science, 170 (2020) |
| 24122 | 0.863 | Bouchut Q.; Appiah K.; Lotfi A.; Dickinson P. | Ensemble One-Vs-One Svm Classifier For Smartphone Accelerometer Activity Recognition | Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018 (2019) |
| 15088 | 0.858 | Cruciani F.; Vafeiadis A.; Nugent C.; Cleland I.; McCullagh P.; Votis K.; Giakoumis D.; Tzovaras D.; Chen L.; Hamzaoui R. | Comparing Cnn And Human Crafted Features For Human Activity Recognition | Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019 (2019) |
| 29572 | 0.857 | Dhammi L.; Tewari P. | Human Activity Recognition Based On Smartphone Sensors- A Comparative Study | 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021 (2021) |