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Elderly activities recognition and classification for applications in assisted living

机译：老年人活动的识别和分类，以辅助生活

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摘要

Assisted living systems can help support elderly persons with their daily activities in order to help them maintain healthy and safety while living independently. However, most current systems are ineffective in actual situation, difficult to use and have a low acceptance rate. There is a need for an assisted living solution to become intelligent and also practical issues such as user acceptance and usability need to be resolved in order to truly assist elderly people. Small, inexpensive and low-powered consumption sensors are now available which can be used in assisted living applications to provide sensitive and responsive services based on users current environments and situations. This paper aims to address the issue of how to develop an activity recognition method for a practical assisted living system in term of user acceptance, privacy (non-visual) and cost. The paper proposes an activity recognition and classification method for detection of Activities of Daily Livings (ADLs) of an elderly person using small, low-cost, non-intrusive non-stigmatize wrist worn sensors. Experimental results demonstrate that the proposed method can achieve a high classification rate (>90%). Statistical tests are employed to support this high classification rate of the proposed method. Also, we prove that by combining data from temperature sensor and/or altimeter with accelerometer, classification accuracy can be improved.
机译：辅助生活系统可以帮助老年人进行日常活动，以帮助他们在独立生活时保持健康和安全。但是，大多数当前系统在实际情况中是无效的，难以使用并且接受率低。需要一种辅助生活解决方案以变得智能，并且还需要解决诸如用户接受度和可用性之类的实际问题，以便真正地帮助老年人。小型，廉价且低功耗的传感器现已上市，可用于辅助生活应用中，以根据用户当前的环境和情况提供敏感和响应迅速的服务。本文旨在解决如何在用户接受度，隐私（非视觉）和成本方面为实用的辅助生活系统开发一种活动识别方法的问题。本文提出了一种活动识别和分类方法，该方法可使用小型，低成本，非侵入式，不带污点的腕戴传感器来检测老年人的日常生活活动（ADL）。实验结果表明，该方法可以达到较高的分类率（> 90％）。统计测试被用来支持所提出方法的高分类率。而且，我们证明了通过将来自温度传感器和/或高度计的数据与加速度计相结合，可以提高分类精度。

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