The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions
Abstract
The design of an activity recognition and monitoring system based on the eWatch, multi-sensor platform worn on different body positions, is presented in this paper.The system identifies the user's activity in realtime using multiple sensors and records the classification results during a day.We compare multiple time domain feature sets and sampling rates, and analyze the tradeoff between recognition accuracy and computational complexity.The classification accuracy on different body positions used for wearing electronic devices was evaluated.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“They demonstrated accuracy ranging from 70% to 90% for six different ambulatory tasks [157].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [157]
Citations
Cites
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Activity Recognition from User-Annotated Acceleration Data
Bao, L., & Intille, S. S. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_1 · Cited by 3,147
Supervised and Unsupervised Discretization of Continuous Features
Dougherty, J., Kohavi, R., & Sahami, M. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50032-3 · Cited by 2,005
Unsupervised, dynamic identification of physiological and activity context in wearable computing
Krause, A., Siewiorek, D. P., Smailagic, A., & Farringdon, J. (2004) · Proceedings · doi:10.1109/iswc.2003.1241398 · Cited by 154
Self-citationeWatch: a wearable sensor and notification platform
Maurer, U., Rowe, A., Smailagic, A., & Siewiorek, D. P. (2006) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2006.24 · Cited by 146
Self-citationEmpirical Study of Design Choices in Multi-Sensor Context Recognition Systems
Bharatula, N. B., & Stäger, M. (2005) · Cited by 37
Power and Size Optimized Multi-Sensor Context Recognition Platform
Bharatula, N. B., Stäger, M., Lukowicz, P., & Tröster, G. (2005) · Proceedings · doi:10.1109/iswc.2005.42 · Cited by 19
Flagged first, then by citations
Cited by
Sorted by citations
Items 201–266 of 266
A Model for Human Activity Recognition in Ambient Assisted Living
do Amaral, W. D., Dantas, M. A. R., & Campos, F. (2019) · Lecture notes in networks and systems · doi:10.1007/978-3-030-33509-0_29 · Cited by 2
Out-of-distribution in Human Activity Recognition
Roy, D., Komini, V., & Girdzijauskas, Š. (2022) · Swedish Artificial Intelligence Society Workshop (SAIS) · doi:10.1109/sais55783.2022.9833052 · Cited by 3
Inhaler Motion Evaluation Via Weighted DP Matching
Hasegawa, A., Shimono, T., Takano, S., Masaki, K., Nakada, H., Nishie, M., & Hakamata, J. (2023) · IEEJ Journal of Industry Applications · doi:10.1541/ieejjia.22004514 · Cited by 2
A Novel Procedure for Recognising Human Activity Using Machine Learning
Singh, N., & Bhatt, M. (2023) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec58147.2023.10276061 · Cited by 2
Islam, S., Bulbul, M. F., & Islam, M. S. (2018) · Machine Learning and Applications An International Journal · doi:10.5121/mlaij.2018.5201 · Cited by 2
Performance of object recognition in wearable videos
Sabater, A., Montesano, L., & Murillo, A. C. (2019) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa.2019.8869019 · Cited by 2
Body Sensor Networks for Activity and Gesture Recognition
Krishnan, N. C., & Panchanathan, S. (2013) · Signals and communication technology · doi:10.1007/978-3-642-40066-7_15 · Cited by 2
Design of a multimodal hearing system
Tessendorf, B., Debevc, M., Derleth, P., Feilner, M., Gravenhorst, F., Roggen, D., Stiefmeier, T., & Tröster, G. (2013) · Computer Science and Information Systems · doi:10.2298/csis120423012t · Cited by 2
A k-nearest neighbors based approach applied to more realistic activity recognition datasets
Figueredo, J. M. C., Garrido, M. C., Martínez‐España, R., & Muñoz, A. (2018) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-180486 · Cited by 2
Monitoring behaviours with model divergence: emailing studies of users with cognitive impairments
Robinson, W. N., Deng, T., & Aria, A. (2020) · Health Systems · doi:10.1080/20476965.2019.1710582 · Cited by 1
Recognizing Seatbelt-Fastening Behavior with Wearable Technology and Machine Learning
Leland, J., Stanfill, E., Cherian, J., & Hammond, T. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451705 · Cited by 2
Footwear-Based Wearable Sensors for Physical Activity Monitoring
Sazonov, E. E. (2012) · Smart sensors, measurement and instrumentation · doi:10.1007/978-3-642-32538-0_4 · Cited by 2
Enabling Self-Reflection with LifelogExplorer: Generating Simple Views from Complex Data
Kocielnik, R., Maggi, F., & Sidorova, N. (2013) · Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques · doi:10.4108/pervasivehealth.2013.251934 · Cited by 2
Activity Classification Using a Single Tri-axial Accelerometer of Smartphone
Heo, S., Kang, K., & Bae, C. (2012) · Lecture notes in electrical engineering · doi:10.1007/978-94-007-5086-9_35 · Cited by 2
Guiding Assistive-Technology Adaptations Through Intelligent Stream Mining of Patient Data
Robinson, W. N., Deng, T., & Aria, A. (2019) · Advances in healthcare information systems and administration book series · doi:10.4018/978-1-7998-2310-0.ch011 · Cited by 2
Low-Power Integrated Circuit for Orientation Independent Acquisitions from Smart Accelerometers
Vita, A. D., Licciardo, G. D., Femia, A., Benedetto, L. D., Rubino, A., & Pau, D. P. (2020) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-37558-4_6 · Cited by 2
An IoT System with Business Card-Type Sensors for Collaborative Learning Analysis
Yamaguchi, S., Ohtawa, S., Oshima, R., Oshima, J., Fujihashi, T., Saruwatari, S., & Watanabe, T. (2022) · Journal of Information Processing · doi:10.2197/ipsjjip.30.238 · Cited by 3
Sensor-based human activity recognition via zero-shot learning
Wang, W. (2019) · doi:10.32657/10356/104645 · Cited by 2
Pires, I. M., García, N. M., Pombo, N. G. C. C., Flórez‐Revuelta, F., Zdravevski, E., & Spinsante, S. (2018) · doi:10.31224/osf.io/k6rxa · Cited by 2
The Review of Human Activity Recognition-Survey
(2023) · International Journal of Media and Networks · doi:10.33140/ijmn.01.01.02 · Cited by 1
Volschenk, F., Vadapalli, H., & van der Haar, D. T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-61066-0_18 · Cited by 1
Sensor Infrastructures for Ambient Assisted Living
Murdoch, O., Wan, J., Abdalla, S., O’Grady, M. J., & O’Hare, G. M. P. (2013) · Springer eBooks · doi:10.1007/978-1-4614-8495-0_20 · Cited by 1
Al-Hajjar, D., Ehaidib, R. A., Muhanna, S. A., Sohibani, M. A., & Al‐Wabil, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20913-5_34 · Cited by 1
Retracted: An efficient human action recognition approach using FCM and random forest
Lakshmi, R. B., & Parthasarathy, S. (2016) · International Conference on Control, Instrumentation, Communication and Computational Technologies · doi:10.1109/iccicct.2016.7987938 · Cited by 1
RetractedHuman Activity Recognition Using Smartphone Sensor Data
Jain, S., Alam, S., & Prabhu, K. S. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-2071-6_42 · Cited by 1
Feature Selection in Recognizing Daily Living Activities Based on Metabolic Equivalent
Nguyen, N. D. (2020) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2020/125922020 · Cited by 1
Methodology of Activity Recognition: Features and Learning Methods
Ahad, M. A. R., Antar, A. D., & Ahmed, M. (2020) · Intelligent systems reference library · doi:10.1007/978-3-030-51379-5_3 · Cited by 1
Wearable Electronics and Embedded Computing Systems for Biomedical Applications
Scilingo, E. P., & Valenza, G. (2017) · MDPI eBooks · doi:10.3390/books978-3-03842-387-4 · Cited by 1
Ortiz, J. (2015) · Springer theses · doi:10.1007/978-3-319-14274-6_3 · Cited by 1
How AI Algorithms Are Being Used in Applications
Mohammed, A., & Mohammad, M. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-8669-7_5 · Cited by 1
Analysis of Multimodal Sensor Systems for Identifying Basic Walking Activities
Mitchell, J. K., Dehghani-Sanij, A. A., Xie, S. Q., & O’Connor, R. J. (2025) · Technologies · doi:10.3390/technologies13040152 · Cited by 2
Recognition of Human Stair Ascent and Descent Activities Based on Extreme Learning Machine
Xiao, W., Lu, Y., Cui, J., & Ji, L. (2014) · Proceedings in adaptation, learning and optimization · doi:10.1007/978-3-319-14066-7_25 · Cited by 1
Phát hiện té ngã cho người cao tuổi bằng gia tốc kế và mô hình học sâu Long Short-Term Memory.
Án, T. C., Phúc, L. M., Đức, Đ. T., Hùng, N. B., Chiến, L. Đ., Diễm, P. T. X., Pha, S. B., & Long, N. H. V. (2017) · Can Tho University Journal of Science · doi:10.22144/ctu.jsi.2017.009 · Cited by 1
A Multiaxial Data-Based Machine Learning Model for Exercise Motion Recognition
Ahn, J.-E., Yi, E.-S., Kim, J., & Lee, B. M. (2018) · International Journal of Control and Automation · doi:10.14257/ijca.2018.11.2.08 · Cited by 1
A Personal Activity Recognition System Based on Smart Devices
Murcia, H., & Triana, J. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-31019-6_41 · Cited by 1
Novel tent pooling based human activity recognition approach
Tuncer, T., & Ertam, F. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09893-4 · Cited by 1
Modelling Human Activity using Smartphone Data
Badrinath, S., & Muthalagu, R. (2021) · The Open Biomedical Engineering Journal · doi:10.2174/1874120702115010058 · Cited by 1
Detecção de novidade em fluxos contínuos de dados multiclasse
Paiva, E. F. D. R. (2014) · doi:10.11606/t.55.2014.tde-14072014-142248 · Cited by 1
Using plantar pressure for free-living posture recognition and sedentary behaviour monitoring
Ricardo, A. G. (2018) · doi:10.32657/10220/47804 · Cited by 1
Activity Classification in Users of Ankle Foot Orthoses
Archer, C. (2013) · doi:10.18130/v35h2s · Cited by 1
Contributions to Human Motion Modeling and Recognition using Non-intrusive Wearable Sensors
Martín, M. (2022) · doi:10.20868/upm.thesis.70493 · Cited by 1
An Injectible Cpw Fed Flexible Radiator With Wireless Charging for Proactive Breast Tumour DetectionPreprint
Chrysolite, S., & Pushpa, A. J. M. (2022) · Research Square · doi:10.21203/rs.3.rs-2250864/v1 · Cited by 1
Cooperating Objects in Healthcare Applications
Karnouskos, S., Marrón, P. J., Fortino, G., Mottola, L., & Dios, J. R. M. (2014) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-642-45401-1_4
A Novel Performance Evaluation Method for Visual Tracking Methods
Teng, H., Yu, L., Liu, H., Sun, F., & Liu, X. (2015) · Lecture notes in electrical engineering · doi:10.1007/978-3-662-46469-4_33
Implementation and pilot testing of an Android-based real-time activity detection system
Sun, Y. (2013) · Digital Repository Service (Northeastern University) · doi:10.17760/d20003208
Multiple Activity Recognition Through Combining Classifier
Aswitha, G. (2018) · i-manager’s Journal on Pattern Recognition · doi:10.26634/jpr.5.1.14232
5-K-Dimensional Tree for Human Activity Recognition in Smart Home
Wang, Z.-J., & Fang, H. (2019) · International Conference on Robotics and Automation Sciences (ICRAS) · doi:10.1109/icras.2019.8808963
Classification of Walkers Based on Back Angle Measurements Using Wireless Sensor Node
Chowdhary, R. S., & Basu, M. (2019) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-34515-0_43
Benablo, C. I. P., & Palaoag, T. D. (2020) · Asia Service Sciences and Software Engineering Conference (ASSE) · doi:10.1145/3399871.3399879
Predicting the type of physical activity from tri-axial smartphone accelerometer data
Pavlović, K. (2021) · Istrazivanja i projektovanja za privredu · doi:10.5937/jaes0-27166
SPINE‐Based Body Sensor Network Applications
Fortino, G., Gravina, R., & Galzarano, S. (2018) · Wiley eBooks · doi:10.1002/9781119078807.ch11
Noor, S., Aamir, M., Ismat, N., & Saleem, M. I. (2022) · Journal of ICT Standardization · doi:10.13052/jicts2245-800x.1023
Smart Fit On Using Machine Learning and Deep Learning Techniques
Kalyani, N. (2023) · International Journal of Computational Sciences and Engineering · doi:10.37622/ijcse/13.1.2023.11-34
Activity Recognition for Attachments of Construction Machinery Using Decision Trees
Theobald, M., & Top, F. (2023) · Lecture notes in civil engineering · doi:10.1007/978-3-031-44021-2_11
Human Activity Recognition Using Sensor Measurements
Karthick Saran, J., Mayilsamy, M., Riyash Ahamed, M., & Munirathinam, R. (2024) · International Conference on Expert Clouds and Applications (ICOECA) · doi:10.1109/icoeca62351.2024.00104
Survey on Objective Measurement and Sensor-Based Detection of Physical and Social Activities
Mahmood, A., Sridevi, P., Meikandan, P. V., & Ahamed, S. I. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00083
Park, J.-H., & Moon, N. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-2447-5_64
Malhotra, V., Popli, R., Kumar, R., Khullar, V., & Kansal, I. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-11488-4_2
A Novel Dataset for Gait Activity Recognition in Real-World Environments
Mitchell, J. C., Dehghani-Sanij, A. A., Shengquan, X., & O’Connor, R. J. (2026) · Sensors · doi:10.3390/s26030833 · Cited by 1
Energy-efficiency in wireless sensor networks
Rault, T. (2015) · doi:10.70675/e1b49a3dzd54az4e25z9f7az7e5e4ddf36f8 · Cited by 22
A personal knowledge base integrating user data and activity timeline
Montoya, D. (2017) · theses.fr (ABES) · doi:10.70675/27f9481bz8023z43e2za15az824b75d65027
Wearable Sensor-Based Physical Activity Recognition
Sheng, Q. Z., Liu, Y., & Yao, L. (2026) · Springer eBooks · doi:10.1007/978-3-032-16502-2_6
A CNN–transformer framework with GAN augmentation for human activity recognition in wearable sensors
Jan, M. U., Khan, B., Shah, A., Amin, M., Wahab, F., & Athar, R. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21739-z
Metadata
- Authors
- Maurer, U., Smailagic, A., Siewiorek, D. P., & Deisher, M.
- Affiliations
- Technical University of Munich (DE)
- Carnegie Mellon University (US)
- Intel (United States) (US)
- Publisher
- Defense Technical Information Center
- Published
- 2006-04-01
- OpenAlex
- W4235485727
- Cited by
- 266
- References
- 7
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 9 · 2023: 16 · 2022: 19 · 2021: 20 · 2020: 25 · 2019: 29 · 2018: 29 · 2017: 21 · 2016: 13 · 2015: 18 · 2014: 17 · 2013: 14 · 2012: 8
- Type
- report
- Language
- en
- Open access
- closed
- Funders
- National Science Foundation (0205266, 0203448)
- Intel Corporation
- Defense Advanced Research Projects Agency (NBCHD-03-0010)
- Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Keywords
- activity recognition, activity monitoring, wearable sensors, multi-sensor fusion, posture, time-domain features, sampling frequency, real-time classification, computational complexity, recognition accuracy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
