Abstract
Activities of daily living (ADL) can be used to identify a person’s daily routine which helps health professionals to provide preventive healthcare. Classification of ADLs is therefore very important. In this study, long short-term memory (LSTM) network, which is an extension of recurrent neural networks, is used. Data collected in MobiAct data set are used to train and test the network. An accuracy of 0.90 is achieved using LSTM network.
Similar content being viewed by others
References
Mathie, M.J., Coster, A.C.F., Lovell, N.H., Celler, B.G., Lord, S.R., Tiedemann, A.: A pilot study of long-term monitoring of human movements in the home using accelerometry. J. Telemed. Telecare 10, 1–8 (2004)
Najafi, B., Aminian, K., Paraschiv-Ionescu, A., Loew, F., Bla, C.J., Robert, P.: Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly. IEEE Trans. Biomed. Eng. 50, 711–723 (2003)
Allen, F.R., Ambikairajah, E., Lovell, N.H., Celler, B.G.: Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models. Physiol. Meas. 27, 935–951 (2006)
Karantonis, D.M., Narayanan, M.R., Mathie, M., Lovell, N.H., Celler, B.G.: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. Inf. Technol. Biomed. 10, 156–167 (2006)
Lindemann, U., Hock, A., Stuber, M., Keck, W., Becker, C.: Evaluation of a fall detector based on accelerometers: a pilot study. Med. Biol. Eng. Comput. 43, 548–551 (2005)
Bourke, A.K., Brien, J.V.O., Lyons, G.M.: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26, 194–199 (2007)
Kangas, M., Konttila, A., Lindgren, P., Winblad, I., Jämsä, T.: Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture 28, 285–291 (2008)
Zheng, Y.: Human activity recognition based on the hierarchical feature selection and classification framework. J. Electr. Comput. Eng. 2015, 9 (2015)
Li, W., Xu, Y., Tan, B., Piechocki, R.J.: Passive wireless sensing for unsupervised human activity recognition in healthcare. In: 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1528–1533 (2017)
Hegde, N., Bries, M., Swibas, T., Melanson, E., Sazonov, E.: Automatic recognition of activities of daily living utilizing insole based and wrist worn wearable sensors. IEEE J. Biomed. Health Inform. 99, 1 (2018)
Vepakomma, P., De, D., Das, S.K., Bhansali, S.: A-Wristocracy: deep learning on wrist-worn sensing for recognition of user complex activities. In: 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 1–6 (2015)
And, B., Baglio, S., Lombardo, C.O., Marletta, V.: A multisensor data-fusion approach for ADL and fall classification. IEEE Trans. Instrum. Meas. 65, 1960–1967 (2016)
Awais, M., Palmerini, L., Chiari, L.: Physical activity classification using body-worn inertial sensors in a multi-sensor setup. In: 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), pp. 1–4 (2016)
Semwal, V.B., Gaud, N., Nandi, G.C.: Human gait state prediction using cellular automata and classification using ELM. MISP, vol. 748, pp. 135–145 (2017)
Semwal, V.B., Raj, M., Nandi, G.C.: Biometric gait identification based on a multilayer perceptron. Robot. Auton. Syst. 65, 65–75 (2015)
Murad, A., Pyun, J.Y.: Deep recurrent neural networks for human activity recognition. Sensors 17(11), 2556 (2017). https://doi.org/10.3390/s17112556
Heffernan, R., Yang, Y., Paliwal, K., Zhou, Y.: Capturing non-local interactions by long short term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers, and solvent accessibility. Struct. Bioinform. 33, 2842–2849 (2017)
Zhang, Y., Xiong, R., He, H., Pecht, M.: Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Trans. Veh. Technol. 99, 1 (2018)
Long short-term memory networks. https://www.mathworks.com/help/nnet/ug/long-short-term-memory-networks.html (2018). Accessed 6 Sep 2018
Vavoulas, G., Chatzaki, C., Malliotakis, T., Pediaditis, M., Tsiknakis, M.: The MobiAct dataset: recognition of activities of daily living using smartphones. In: International Conference on Information and Communication Technologies for Ageing Well and e-Health, pp. 143–151 (2016)
Chen, Y., Zhong, K., Zhang, J., Sun, Q., Zhao, X.: LSTM networks for mobile human activity recognition. In: International Conference on Artificial Intelligence: Technologies and Applications (ICAITA) (2016)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Malshika Welhenge, A., Taparugssanagorn, A. Human activity classification using long short-term memory network. SIViP 13, 651–656 (2019). https://doi.org/10.1007/s11760-018-1393-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-018-1393-7