Abstract
Analysis of human movement is an important research area, specially for health applications. In order to assess the quality of life of people with mobility problems like Parkinson’s disease (PD) or stroke patients, it is crucial to monitor their daily life activities. The main goal of this work is to characterize basic activities and their transitions using a single sensor located at the waist. This paper presents a novel postural detection algorithm which is able to detect and identify 6 different postural transitions, sit to stand, stand to sit, bending up/down and lying to sit and sit to lying transitions with a sensitivity of 86.5% and specificity of 95%. The algorithm has been tested on 31 healthy volunteers and 8 PD patients who performed a total of 545 and 176 transitions respectively. The proposed algorithm is suitable to be implemented in real-time systems for on-line monitoring applications.
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Najafi, B., Aminian, K., Paraschiv-Ionescu, A., Loew, F., Büla, C., Robert, P.: Ambulatory System for Human Motion Analysis Using a Kinematic Sensor: Monitoring of Daily Physical Activity in the Elderly. IEEE Transactions on Biomedical Engineering 50, 711–723 (2003)
Kralj, A., Jaeger, R.J., Munih, M.: Analysis of standing up and sitting down in humans: Definitions and Normative Data Representation. Journal of Biomechanics 23(11), 1123–1138 (1990)
Cheng, P.T., Liaw, M.Y., Wong, M.K., Tang, F.T., Lee, M.Y., Lin, P.S.: The sit-to-stand movement in stroke patients and its correlation with falling. Archives of Physical Medicine and Rehabilitation 79(9), 1043–1046 (1998)
Nyberg, L., Gustafson, Y.: Patient falls in stroke rehabilitation. A challenge to rehabilitation strategies. Stroke 26(5), 838–842 (1995)
Wheeler, J., Woodward, C., Ucovich, R.L., Perry, J., Walker, J.M.: Rising from a chair. Influence of age and chair design. Physical Therapy 65(1), 22–26 (1985)
Guzmán, R.A., Prado, H.E., Porcel, H., Cordier, B.: Differences in the momentum development during transfers sit to stand between fall and no fall elderly. Geriatr. Gerontol. 44(4), 200–204 (2009)
Kerr, K.M., White, J.A., Barr, D.A., Mollan, R.A.B.: Analysis of the sit-stand-sit movement cycle: development of a measure system. Gait & Posture 2(3), 173–181 (1994)
Bidargaggi, N., Klingbeil, L., Sarela, A., Boyle, J., Cheung, V., Yelland, C., Karunanithi, M., Gray, L.: Wavelet based approach for posture transition estimation using a waist worn accelerometer. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, pp. 1884–1887 (2007)
Najafi, B., Aminian, K., Loew, F., Blanc, Y., Robert, P.: Measurement of Stand-Sit and Sit-Stand Transitions Using a Miniature Gyroscope and Its Application in Fall Risk Evaluation in the Elderly. IEEE Transactions on Biomedical Engineering 49(8), 843–851 (2002)
Bieber, G., Koldrack, P., Sablowski, C., Peter, C., Urban, B.: Mobile physical activity recognition of Stand-up and Sit-down Transitions for user behavior analysis. In: Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments, article n. 50 (2010)
Karantonis, D.M., Narayanan, M.R., Mathie, M., Nigel, H.L., Celler, B.G.: Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring. IEE Transactions on Information technology in Biomedicine 70(1), 156–167 (2006)
Samà, A., Perez, C., Rodriguez-Martin, D., Cabestany, J., Moreno Aróstegui, J.M., Rodríguez-Molinero, A.: A Heterogeneous Database for Movement Knowledge Extraction in Parkinson’s Disease. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2013)
REMPARK–Personal Health Device for the Remote and Autonomous Management of Parkinson’s Disease FP7-ICT-2011-7-287677
Samà, A., Angulo, C., Pardo, D., Català, A., Cabestany, J.: Analyzing human gait and posture by combining feature selection and kernel methods. Neurocomputing 74(16), 2665–2674 (2011)
MOMOPA–Monitoring the Mobility of Parkinson’s Patients for Therapeutic Purposes FIS. ISCIII. 2009-2010
Zhou, S., Shan, Q., Fei, F., Li, W.J., Pin Kwong, C., Wu, P.C.K., Meng, B., Chan, C.K.H., Liou, J.Y.J.: Gesture Recognition for Interactive Controllers Using MEMS Motion Sensors. In: Proceedings of the 2009 4th IEEE International Conference on Nano/Micro Engineered and Molecular Systems, pp. 935–940 (2009)
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Martín, D.R., Samá, A., López, C.P., Catalá, A., Cabestany, J., Molinero, A.R. (2013). Identification of Postural Transitions Using a Waist-Located Inertial Sensor. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_17
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DOI: https://doi.org/10.1007/978-3-642-38682-4_17
Publisher Name: Springer, Berlin, Heidelberg
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