Enabling IoT for In-Home Rehabilitation: Accelerometer Signals Classification Methods for Activity and Movement Recognition

被引:89
作者
Bisio, Igor [1 ]
Delfino, Alessandro [1 ]
Lavagetto, Fabio [1 ]
Sciarrone, Andrea [1 ]
机构
[1] Univ Genoa, Dept Elect Elect Telecommun Engn & Naval Architec, I-16145 Genoa, Italy
来源
IEEE INTERNET OF THINGS JOURNAL | 2017年 / 4卷 / 01期
关键词
Accelerometer signal classification; activity recognition (AR); eHealth and mHealth; movement recogni-tion (MR); smartphones; HEALTH; WALKING;
D O I
10.1109/JIOT.2016.2628938
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Rehabilitation and elderly monitoring for active aging can benefit from Internet of Things (IoT) capabilities in particular for in-home treatments. In this paper, we consider two functions useful for such treatments: 1) activity recognition (AR) and 2) movement recognition (MR). The former is aimed at detecting if a patient is idle, still, walking, running, going up/down the stairs, or cycling; the latter individuates specific movements often required for physical rehabilitation, such as arm circles, arm presses, arm twist, curls, seaweed, and shoulder rolls. Smartphones are the reference platforms being equipped with an accelerometer sensor and elements of the IoT. The work surveys and compares accelerometer signals classification methods to enable IoT for the aforementioned functions. The considered methods are support vector machines (SVMs), decision trees, and dynamic time warping. A comparison of the methods has been proposed to highlight their performance: all the techniques have good recognition accuracies and, among them, the SVM-based approaches show an accuracy above 90% in the case of AR and above 99% in the case of MR.
引用
收藏
页码:135 / 146
页数:12
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