The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Elderly activities recognition and classification for applications in assisted living
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Learning Pattern Classification Tasks with Imbalanced Data Sets
Hoang, G., Bouzerdoum, A., & Lam, S. (2009) · Pattern Recognition · doi:10.5772/7544 · Cited by 178
Research works printed but not linked: 30
- Aarts, 2004, Ambient intelligence: A multimedia perspective, IEEE Multimedia, 11, 12, 10.1109/MMUL.2004.1261101 · DOI · Paper page
- Banos, 2012, Daily living activity recognition based on statistical feature quality group selection, Expert Systems with Applications, 39, 8013, 10.1016/j.eswa.2012.01.164 · DOI
- Botia, 2012, Ambient assisted living system for in-home monitoring of healthy independent elders, Expert Systems with Applications, 39, 8136, 10.1016/j.eswa.2012.01.153 · DOI
- Chaaraoui, 2012, A review on vision techniques applied to human behaviour analysis for ambient-assisted living, Expert Systems with Applications, 39, 10873, 10.1016/j.eswa.2012.03.005 · DOI · Paper page
- Chernbumroong, S., Atkins, A. S., & Yu, H. (2010). Perception of smart home technologies to assist elderly people. In The fourth international conference on software, knowledge, information management and applications (SKIMA 2010) (pp. 90–97).
- Costa, 2012, Sensor-driven agenda for intelligent home care of the elderly, Expert Systems with Applications, 39, 12192, 10.1016/j.eswa.2012.04.058 · doi:10.1016/j.eswa.2012.04.058
- Demiris, 2008, Senior residents’ perceived need of and preferences for “smart home” sensor technologies, International Journal of Technology Assessment in Health Care, 24, 120, 10.1017/S0266462307080154 · DOI · Paper page
- Demiris, 2004, Older adults’ attitudes towards and perceptions of’smart home’technologies: A pilot study, Informatics for Health and Social Care, 29, 87, 10.1080/14639230410001684387 · DOI · Paper page
- Fleury, 2010, Svm-based multimodal classification of activities of daily living in health smart homes: Sensors, algorithms, and first experimental results, IEEE Transactions on Information Technology in Biomedicine, 14, 274, 10.1109/TITB.2009.2037317 · DOI · Paper page
- Fuentes, 2012, Online motion recognition using an accelerometer in a mobile device, Expert Systems with Applications, 39, 2461, 10.1016/j.eswa.2011.08.098 · DOI
- Guyon, 2003, An introduction to variable and feature selection, The Journal of Machine Learning Research, 3, 1157
- Györbı´ró, 2009, An activity recognition system for mobile phones, Mobile Networks and Applications, 14, 82, 10.1007/s11036-008-0112-y · DOI · Paper page
- Hong, 2009, Evidential fusion of sensor data for activity recognition in smart homes, Pervasive and Mobile Computing, 5, 236, 10.1016/j.pmcj.2008.05.002 · DOI · Paper page
- Hong, 2010, Mobile health monitoring system based on activity recognition using accelerometer, Simulation Modelling Practice and Theory, 18, 446, 10.1016/j.simpat.2009.09.002 · DOI
- Hsu, 2002, A comparison of methods for multiclass support vector machines, IEEE Transactions on Neural Networks, 13, 415, 10.1109/72.991427 · DOI · Paper page
- Huang, 2010, A flexible sequence alignment approach on pattern mining and matching for human activity recognition, Expert Systems with Applications, 37, 298, 10.1016/j.eswa.2009.05.057 · DOI
- Kwon, 2012, Single activity sensor-based ensemble analysis for health monitoring of solitary elderly people, Expert Systems with Applications, 39, 5774, 10.1016/j.eswa.2011.11.090 · doi:10.1016/j.eswa.2011.11.090
- Mahoney, 1965, Functional evaluation: The barthel index, Maryland State Medical Journal, 14, 56
- Martı´nez-Pérez, 2012, Activity inference for ambient intelligence through handling artifacts in a healthcare environment, Sensors, 12, 1072, 10.3390/s120101072 · doi:10.3390/s120101072
- Maurer, 2006, Location and activity recognition using ewatch: A wearable sensor platform, Ambient Intelligence in Everyday Life, 86, 10.1007/11825890_4 · DOI
- Najafi, 2003, Ambulatory system for human motion analysis using a kinematic sensor: Monitoring of daily physical activity in the elderly, IEEE Transactions on Biomedical Engineering, 50, 711, 10.1109/TBME.2003.812189 · DOI · Paper page
- Olivieri, 2012, Eigenspace-based fall detection and activity recognition from motion templates and machine learning, Expert Systems with Applications, 39, 5935, 10.1016/j.eswa.2011.11.109 · DOI
- Parkka, 2006, Activity classification using realistic data from wearable sensors, IEEE Transactions on Information Technology in Biomedicine, 10, 119, 10.1109/TITB.2005.856863 · DOI · Paper page
- Poh, M. (2011). Continuous assessment of epileptic seizures with wrist-worn biosensors. Ph.D. thesis. Massachusetts Institute of Technology.
- Pollack, 2003, Autominder: An intelligent cognitive orthotic system for people with memory impairment, Robotics and Autonomous Systems, 44, 273, 10.1016/S0921-8890(03)00077-0 · DOI · Paper page
- United Nations, D. o. E., & Social Affairs, P. D. (2011). World population prospects: The 2010 revision, cd-rom edition.
- Wang, 2000, Assessing the impact of input features in a feedforward neural network, Neural Computing and Applications, 9, 101, 10.1007/PL00009895 · doi:10.1007/pl00009895
- Yin, 2008, Sensor-based abnormal human-activity detection, IEEE Transactions on Knowledge and Data Engineering, 20, 1082, 10.1109/TKDE.2007.1042 · DOI · Paper page
- Yu, 2003, An adaptive shared control system for an intelligent mobility aid for the elderly, Autonomous Robots, 15, 53, 10.1023/A:1024488717009 · DOI
- Zhu, 2011, Motion- and location-based online human daily activity recognition, Pervasive Mobile Computing, 7, 256, 10.1016/j.pmcj.2010.11.004 · DOI
Cited by
A survey on wearable sensor modality centred human activity recognition in health care
Wang, Y., Cang, S., & Yu, H. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.04.057 · Cited by 409
Self-citationNweke, H. F., Teh, Y. W., Mujtaba, G., & Al-garadi, M. A. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.06.002 · Cited by 394
A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone
Wang, A., Chen, G., Yang, J., Zhao, S., & Chang, C.-Y. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2545708 · Cited by 324
Blockchain-based Personal Health Data Sharing System Using Cloud Storage
Zheng, X., Mukkamala, R. R., Vatrapu, R., & Ordieres-Mere, J. (2018) · 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2018.8531125 · Cited by 281
Peng (2018) · doi:10.1145/3214277 · Cited by 134
Metadata
- Authors
- Chernbumroong, S., Cang, S., Atkins, A., & Yu, H.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2013-04
- Volume
- 40
- Issue
- 5
- Pages
- 1662-1674
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W1969069964
- Cited by
- 317
- References
- 2
- FWCI
- 16.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Elderly activities recognition and classification for applications in assisted living. https://app.cochat.ai/papers/10.1016/j.eswa.2012.09.004
- The paper
- Chernbumroong, S., Cang, S., Atkins, A., & Yu, H. (2013). Elderly activities recognition and classification for applications in assisted living. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2012.09.004
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.