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Chen, S., Lach, J., Lo, B. & Yang, G. Z. Toward common amble assay with wearable sensors: A analytical review. IEEE J. Biomed. Heal. Informatics.20, 1521–1537 (2016).
Tao, W., Liu, T., Zheng, R. & Feng, H. Amble assay application wearable sensors. Sensors.12, 2255–2283 (2012).
Muro-de-la-Herran, A., García-Zapirain, B. & Méndez-Zorrilla, A. Amble assay methods: An overview of wearable and non-wearable systems, highlighting analytic applications. Sensors.14, 3362–3394 (2014).
Norris, M., Anderson, R. & Kenny, I. C. Method assay of accelerometers and gyroscopes in active gait: A analytical review. Proc. Inst. Mech. Eng. Part P J. Sport. Eng. Technol.228, 3–15 (2014).
Granhed, H., Altgarde, E., Akyurek, L. M. & David, P. Injuries abiding by falls-a review. Trauma Acute Care.2, 38–42 (2017).
Li, W. et al. Outdoor avalanche amid middle-aged and earlier adults: a alone accessible bloom problem. Am J Accessible Health.96(7), 1192–1200 (2006).
Wang, J., Chen, Y., Hao, S., Peng, X. & Hu, L. Deep acquirements for sensor-based action recognition: A survey. Pattern Recognit. Lett.119, 3–11 (2019).
Dehzangi, O., Taherisadr, M. & ChangalVala, R. IMU-based amble acceptance application convolutional neural networks and multi-sensor fusion. Sensors.17, 2735 (2017).
Zhang, C., Liu, W., Ma, H. & Fu, H. Siamese neural arrangement based amble acceptance for animal identification. ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. – Proc. 2016-May, 2832–2836 (2016).
Weiss, A. et al. Toward automated, calm appraisal of advancement amid patients with Parkinson disease, application a body-worn accelerometer. Neurorehabil. Neural Repair.25, 810–818 (2011).
Sprager, S. & Juric, M. B. An able HOS-based amble affidavit of accelerometer data. IEEE Trans. Inf. Forensics Secur.10, 1486–1498 (2015).
Gadaleta, M. & Rossi, M. IDNet: Smartphone-based amble acceptance with convolutional neural networks. Pattern Recognition.74, 25–37 (2018).
Dixon, P. C. et al. Amble adaptations of earlier adults on an asperous brick apparent can be predicted by age-related physiological changes in strength. Amble Posture.61, 257–262 (2018).
Zurales, K. et al. Amble ability on an asperous apparent is associated with avalanche and abrasion in earlier capacity with a spectrum of lower limb neuromuscular function: a -to-be study. Am. J. Phys. Med. Rehabil.95, 83–90 (2016).
Thies, S. B., Richardson, J. K. & Ashton-Miller, J. A. Effects of apparent abnormality and lighting on footfall airheadedness during gait: A abstraction in advantageous adolescent and earlier women. Amble Posture.22, 26–31 (2005).
Yang, A. Y., Jafari, R., Sastry, S. S. & Bajcsy, R. Distributed acceptance of animal accomplishments application wearable motion sensor networks. Journal of Ambient Intelligence and Smart Environments.1(2), 103–115 (2009).
Roggen, D. et al. Collecting circuitous action datasets in awful affluent networked sensor environments. IEEE 2010 – 7th All-embracing Appointment on Networked Sensing Systems(INSS). 233–240 (2010).
Altun, K., Barshan, B. & Tunçel, O. Comparative abstraction on classifying animal activities with miniature inertial and alluring sensors. Pattern Recognition.43(10), 3605–3620 (2010).
Zhang, M. & Sawchuk, A. A. USC-HAD: a circadian action dataset for all-over action acceptance application wearable sensors. Proceedings of the 2012 ACM Appointment on All-over Computing. 1036-1043 (2012).
Reiss, A. & Stricker, D. Introducing a new benchmarked dataset for action monitoring. Proceedings – All-embracing Symposium on Wearable Computers (ISWC). 108–109 (2012).
Casale, P., Pujol, O. & Radeva, P. Personalization and user assay in wearable systems application bio-metric walking patterns. Claimed and All-over Computing.16(5), 563–580 (2012).
Anguita, D., Ghio, A., Oneto, L., Parra, X. & Reyes-Ortiz, J. L. A accessible area dataset for animal action acceptance application smartphones. ESANN 2013 Proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. April, 437–442 (2013).
Ravi, D., Wong, C., Lo, B. & Yang, G. Z. Deep acquirements for animal action recognition: A ability able accomplishing on low-power devices. 2016 IEEE 13th all-embracing appointment on wearable and implanta-ble anatomy sensor networks (BSN). 71–76 (2016).
Weiss, G. M., Yoneda, K. & Hayajneh, T. Smartphone and Smartwatch-Based Biometrics Application Activities of Circadian Living. IEEE Access.7, 133190–133202 (2019).
Bächlin, M. et al. Wearable abettor for Parkinsons ache patients with the freezing of amble symptom. IEEE Transactions on Information Technology in Biomedicine.14(2), 436–446 (2010).
Frank, J., Mannor, S., Pineau, J. & Precup, D. Time Series Assay Application Geometric Template Matching. IEEE Transactions on Pattern Assay and Machine Intelligence.35(3), 740–754 (2013).
Ngo, T. T., Makihara, Y., Nagahara, H., Mukaigawa, Y. & Yagi, Y. The better inertial sensor-based amble database and achievement appraisal of gait-based claimed authentication. Pattern Recognition.47(1), 228–237 (2014).
Zhang, Y. et al. Accelerometer-based amble acceptance by dispersed representation of signature credibility with clusters. IEEE Transactions on Cybernetics.45(9), 1864–1875 (2015).
Subramanian, R. et al. Orientation invariant amble analogous algorithm based on the Kabsch alignment. 2015 IEEE All-embracing Appointment on Identity, Security and Behavior Assay (ISBA). 1–8 (2015).
Marsico, M. D. & Mecca, A. A assay on amble acceptance via wearable sensors. ACM Computing Surveys.52(4), 1–39 (2019).
Luo, Y. et al. A database of animal amble achievement on aberrant and asperous surfaces calm by wearable sensors. figshare https://doi.org/10.6084/m9.figshare.c.4892463 (2020).
Lee, J., Shin, S. Y., Ghorpade, G., Akbas, T. & Sulzer, J. Sensitivity allegory of inertial to optical motion abduction during gait: implications for tracking recovery. 2019 IEEE 16th All-embracing Appointment on Rehabilitation Robotics (ICORR). 139–144 (2019).
Dixon, P. C., Loh, J. J., Michaud-Paquette, Y. & Pearsall, D. J. biomechZoo: An open-source toolbox for the processing, analysis, and decision of biomechanical movement data. Comput. Meth. Prog. Biomed.140, 1–10 (2017).
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