Limb Movements Classification Using Wearable Wireless Transceivers

作者：Guraliuc Anda R^*; Barsocchi Paolo; Potorti Francesco; Nepa Paolo

来源：IEEE Transactions on Information Technology in Biomedicine, 2011, 15(3): 474-480.

DOI：10.1109/TITB.2011.2118763

• Classification of human limbs activities
• K-nearest neighbor (K-NN)
• received signal strength (RSS)
• support vector machine (SVM)
• wearable wireless devices

A feasibility study, where small wireless transceivers are used to classify some typical limb movements used in physical therapy processes is presented. Wearable wireless low-cost commercial transceivers operating at 2.4GHz are supposed to be widely deployed in indoor settings and on people's bodies in tomorrow's pervasive computing environments. The key idea of this work is to exploit their presence by collecting the received signal strength measured between those worn by a person. The measurements are used to classify a set of kinesiotherapy activities. The collected data are classified by using both support vector machine and K-nearest neighbor methods, in order to recognise the different activities.

• 出版日期2011-5