The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm
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Citations
Cites
Internet of Things in industries: A survey
Xu, L. D., He, W., & Li, S. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2300753 · Cited by 5,172
A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring
Yang, C.-C., & Hsu, Y.-L. (2010) · Sensors · doi:10.3390/s100807772 · Cited by 1,058
Data Mining for Internet of Things: A Survey
Tsai, C.-W., Lai, C.-F., Chiang, M.-C., & Yang, L. T. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.103013.00206 · Cited by 620 · cited 1×
“IoT is applied in many applications to get better results [8–10].”
BackgroundFine-Grained Activity Recognition by Aggregating Abstract Object Usage
Patterson, D. J., Fox, D., Kautz, H., & Philipose, M. (2005) · Ninth IEEE International Symposium on Wearable Computers (ISWC'05) · doi:10.1109/iswc.2005.22 · Cited by 452
Pasluosta, C. F., Gassner, H., Winkler, J., Klucken, J., & Eskofier, B. M. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2461555 · Cited by 323
Bridging e-Health and the Internet of Things: The SPHERE Project
Zhu, N., Diethe, T., Camplani, M., Tao, L., Burrows, A., Twomey, N., Kaleshi, D., Mirmehdi, M., Flach, P., & Craddock, I. (2015) · IEEE Intelligent Systems · doi:10.1109/mis.2015.57 · Cited by 242
Cited by
Sensor-based and vision-based human activity recognition: A comprehensive survey
Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107561 · Cited by 730
Kumar, P. M., Lokesh, S., Varatharajan, R., Chandra Babu, G., & Parthasarathy, P. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.04.036 · Cited by 363
Self-citationAcharya, U. R., Fernandes, S. L., WeiKoh, J. E., Ciaccio, E. J., Fabell, M. K. M., Tanik, U. J., Rajinikanth, V., & Yeong, C. H. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1428-9 · Cited by 325
Kumar, P. M., Gandhi, U., Varatharajan, R., Manogaran, G., R., J., & Vadivel, T. (2019) · Cluster Computing · doi:10.1007/s10586-017-1323-4 · Cited by 154
Self-citationRetractedWearable Skin Sensors and Their Challenges: A Review of Transdermal, Optical, and Mechanical Sensors
Ahmad Tarar, A., Mohammad, U., & K. Srivastava, S. (2020) · Biosensors · doi:10.3390/bios10060056 · Cited by 119
Metadata
- Authors
- Varatharajan, R., Manogaran, G., Priyan, M. K., & Sundarasekar, R.
- Venue
- Cluster Computing
- Published
- 2017-06-22
- Volume
- 21
- Issue
- 1
- Pages
- 681-690
- ISSN
- 1386-7857 (ISSN-L)
- 1573-7543
- OpenAlex
- W2652015611
- Cited by
- 277
- References
- 6
- FWCI
- 17.11
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. https://app.cochat.ai/papers/10.1007/s10586-017-0977-2
- The paper
- Varatharajan, R., Manogaran, G., Priyan, M. K., & Sundarasekar, R. (2018). Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Cluster Computing. https://doi.org/10.1007/s10586-017-0977-2
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.
