Development and validation of a dynamic time warping based algorithm to evaluate motion similarity for home-based fall interventions in older people

Abstract

The global healthcare system is under enormous pressure because of rapid trend in population aging and falls cause substantial health and safety problem among elderly people. Although the effectiveness of conventionally prescribed exercises on fall risk reduction and motor function improvement has been well accepted, many elderly people still face difficulty engaging in such exercises for a long period of time due to passive nature of the conventional exercise, inconvenience, cost and lack of skill. One solution to address these barriers is to develop an interactive, home-based, low-cost gamified system for fall intervention by the mode of virtual coaching. Because there is no real coach in such a system and older people are not familiar with most of the new technologies, scientific algorithm for motion similarity evaluation, and good technology acceptance of such a system are very critical to make it be a practical application at home environment. The major objective of this study is to provide effective home-based virtual coaching offering similar outcome to those obtained from conventional fall intervention programs under the supervision of a real coach. The algorithm for performance evaluation should not only take account of different speeds (movement paces) between an elderly user and a virtual coach, but also provide meaningful overall performance evaluation and informative feedback to achieve just-in-time coaching. In this study, we first proposed a dynamic time warping (DTW) based algorithm for evaluation of motion similarity between motion time-series data from an individual user and a virtual coach. The effectiveness of the proposed algorithm was validated in a follow-up motion capture experiment on twenty-one subjects (age: 55±10) from the Tai-chi academy. In this experiment, each subject played an 8-form Tai-chi exergame and their motions were recorded by Xsens 3D motion tracking system and Microsoft Kinect V2. Subject’s performance of Tai-chi automatically evaluated by our proposed algorithm was compared with the one evaluated by three licensed Tai-chi experts. The experimental result showed that the performance evaluation from our algorithm has a strong positive linear relationship (r = 0.89) with experts’ evaluation, which suggested that our algorithm could be effectively used for performance evaluation of an individual when performing home-based rehabilitation exercises. We are continuing to further develop an interactive, home-based, low-cost gamified system for fall intervention for minimizing the risk of falling and improving the life quality of older people.