Development of cost effective Gait mat for assessment of Depression

Abstract

World Health Organization (WHO) estimates that approximately 5% of adults suffer from mental issues related to depression. The rate of increase in the number of patients suffering from depression is significantly higher that the available medical practitioners (specifically psychiatrists). As a result, diagnosis and treatment can be expensive. Alternative methods are being looked into for assessment of depression. In recent times, emerging technologies like Virtual Reality (VR) has been proposed to assess mental health issues in therapeutic settings. VR based applications provide personalized motivation and meaningful engagement for assessment and treatment. External sensors or devices aid VR applications in assessing physiological and behavioural measures related to depression. One behavioural measure is gait i.e., the pattern of walking of an individual. Gait has been identifed as an important parameter that changes with the mental state of a person and gait analysis techniques can be used to aid medical practitioners in identification of depression. The focus of this thesis is on designing and building a low-cost customizable mat for gait analysis. As part of the thesis, we study the use of Virtual Reality technology in healthcare in general and more specifically on the detection of depression. We performed a systematic literature review, the results from the review helped us to categorise the different physiological and behavioural measures related to depression. We put together a list of VR applications that were used in the assessment of depression and developed a bare minimum checklist to design the scenes within VR applications for diagnosing depression. We developed a mat using Velostat, a piezoresistive based low-cost sensing material for performing gait analysis. Initially, small size mats were designed and were analysed for crosstalk, full scan time, sensitivity and range. Then, a 1.8 x 1.8 feet mat was designed, and one of the gait parameters was calculated from it and compared with a commercial mat called Sensing Tex with an accuracy of 95%. After multiple iterations, we designed a 10.5 x 2 feet pressure sensing mat and used it to perform experiments. VR applications with scenes that evoke emotions like happiness, sadness and calmness were designed and used in the experiments. The results of the experiments showed positive as well as negative correlations between different gait parameters and their PHQ-9 values. Additionally differences in gait like the differences in stride length, velocity, and cadence were observed in the depressed and the non depressed were using descriptive statistics.However, inferential statistics were not conducted due to the limited sample size.