Automated Analysis of Functional Balance
Keywords: Balance assessment, computer vision, functional assessment
Overview of Research
Balance and gait in the older adult population need special attention since they affect their functional mobility and safety. Previous studies have shown that most older adults report some difficulties with balance and these difficulties associate with falls in this age group (Hausdorff, Rios, & Edelberg, 2001).
Consequently, there is a need for measures of gait to monitor the recovery status of older adults over time and to assess their balance. On the other hand, typical gait analysis used in the clinic or lab cannot reflect long term changes in balance since balance failures often take place in homes, where standardized assessment cannot be applied. Moreover, tasks performed in the gait laboratory or clinics do not always simulate normal daily activities of older adult as people may feel more distracted at home than in the laboratory. One strategy working to address this limitation is the development of an affordable automated tool to better understand rehabilitation in home, capture balance failures taking place in home and reduce the frequency of clinical visits. To this end, the proposed study aims to develop an automated balance assessment tool which performs longitudinal, quantitative analyses of functional balance in a person’s own home through the analysis of common activities and movements.
According to the above introduction and the overall goal, specific objectives of this study are to:
- Determine a “Gold Standard” currently used by therapists to assess the balance of older adults who have suffered a stroke, and how these map onto common activities and movements that may be performed in the home.
- Develop an automated tool which will perform longitudinal and quantitative analyses of functional balance of older adults who have suffered a stroke through capturing and analysing functional activities known to reveal balance change.
- Validate the accuracy and investigate the generalizability of the automated tool in evaluation of balance on an independent and unseen population.
- Explore how this automated tool can be deployed in client’s home and how it captures expert determined levels of balance performance over long term.
Our system includes the Microsoft Kinect (Figure 1), which will be deployed in the home to capture functional activities known to reveal balance change (e.g. gait, sit-to-stands). Several movement features and kinematic quantities can be computed from recorded joint positions using the Kinect via processing of 3D skeletal sequence. Using the machine learning algorithms, we will design a classifier to translate the reconstructed kinematics onto assessments of motor quality that are consistent with “gold standard” functional balance measures.
Figure 1. Using a Kinect camera to survey a home environment.
Several movement features and kinematic quantities can be computed from recorded joint positions using the Kinect via processing of 3D skeletal sequence. Using the machine learning algorithms, we will design a classifier to translate the reconstructed kinematics onto assessments of motor quality that are consistent with “gold standard” functional balance measures.
Figure 2 is an example of the stride length measured on a balance impaired individual after Total Hip Replacement surgery across the spectrum of recovery, from pre-operative to 9 weeks post-operative assessment. As shown in the figure, the measured gait parameters were all worsened in comparison with pre-operative values after the surgery and all started to improve from 1 week to 6 weeks following the surgery. The preliminary results demonstrated the feasibility of using this type of system (specifically a Microsoft Kinect sensor and custom developed machine learning algorithms) for inferring the kinematics of functional activity recorded over time and being able to differentiate the different stages of recovery post surgery.
Figure 2. Example of change in gait parameters captured using the balance assessment system [click to enlarge image].
NSERC CARE scolarship
Elham Dolatabadi, University of Toronto
Alex Mihailidis, University of Toronto
Karl Zabjek, University of Toronto
Babak Taati, Toronto Rehabilitation Institute