Vision-Based Assessment of Gait Features Associated With Falls in People With Dementia

Abstract

Background Gait impairments contribute to falls in people with dementia. In this study, we used a vision-based system to record episodes of walking over a 2-week period as participants moved naturally around their environment, and from these calculated spatiotemporal, stability, symmetry, and acceleration gait features. The aim of this study was to determine whether features of gait extracted from a vision-based system are associated with falls, and which of these features are most strongly associated with falling. Methods Fifty-two people with dementia admitted to a specialized dementia unit participated in this study. Thirty different features describing baseline gait were extracted from Kinect recordings of natural gait over a 2-week period. Baseline clinical and demographic measures were collected, and falls were tracked throughout the participants’ admission. Results A total of 1,744 gait episodes were recorded (mean 33.5 ± 23.0 per participant) over a 2-week baseline period. There were a total of 78 falls during the study period (range 0–10). In single variable analyses, the estimated lateral margin of stability, step width, and step time variability were significantly associated with the number of falls during admission. In a multivariate model controlling for clinical and demographic variables, the estimated lateral margin of stability (p = .01) was remained associated with number of falls. Conclusions Information about gait can be extracted from vision-based recordings of natural walking. In particular, the lateral margin of stability, a measure of lateral gait stability, is an important marker of short-term falls risk.

Predicting TUG score from gait characteristics with video analysis and machine learning

“…Mehdizadeh, et al studied whether the gait characteristics extracted from Kinect vision system are associated with number of fall during two week [18]. The gait characteristics include five categories: spatial-temporal, variability, symmetry, stability, and acceleration frequency domain.…”

Inertial sensor-based centripetal acceleration as a correlate for lateral margin of stability during walking and turning

“…Initial contact was defined as the maximal distance between the heel and sacrum marker in fore direction [29], and the MoS at initial contact was extracted for each step as the primary outcome. Only the MoS at initial contact was considered as previous reports have indicated the significance of this event in locomotor control [17] and fall-risk assessments [13]. All XcoM, BoS, and MoS values were oriented relative to the position and of the CoM; Positive MoS occured when the BoS was to the left of the XcoM, and negative MoS when the BoS was to the right of the XcoM, regardless of stance limb.…”

“…While other spatiotemporal gait outcomes were assessed, the estimated lateral MoS was the only gait measure associated with prospective falls [13]. Data from Mehdizadeh and colleagues [13] support quantifying the lateral MoS in community settings to assess fall-risk. Yet, the novel, low-cost camera system used in that study relies on line of sight and cannot assess MoS in every environment.…”

“…The relationship between the XcoM and the base of support (BoS) reveals the instantaneous mechanical stability of the system; if the XcoM falls outside the BoS, balance cannot be recovered with joint torque alone -a stepping response or external force / torque is required. Since the spatial distance between the XcoM and the BoS was defined as the margin of stability (MoS) [7,8], the MoS has been widely used to assess gait stability [6,[9][10][11][12][13][14][15], and gait controllers have been proposed with objectives of maintaining constant MoS, or constant offset, through foot placement [16,17].…”

“…Recently, the lateral MoS was estimated in a communitydwelling setting using camera-based systems [13]. While other spatiotemporal gait outcomes were assessed, the estimated lateral MoS was the only gait measure associated with prospective falls [13]. Data from Mehdizadeh and colleagues [13] support quantifying the lateral MoS in community settings to assess fall-risk.…”