Elderly 3D balance and gait analysis with a simple depth-camera system

点击翻译按钮获取中文摘要

Projections for 2031 show that in Canada, people over 65 will reach nearly 25% in 2031. This major demographic transformation will have important repercussions. As the Canadian population is aging, medical care and indirect costs from falls and musculoskeletal problems will increase. For instance, one of the greatest hazards for elderly is accidental falls and 62% of injury-related hospitalizations for seniors are the result of falls. It is therefore essential to innovate in the area of elder healthcare to identify new technologies to support the elderly. In this context, the general long-term objective of this program of research is to develop a low-cost, easy-to-use and portable video system to identify relevant clinical parameters in elder balance and gait for early fall risk assessment and prevention for use in a clinical setting. This will be achieved by considering the following three specific objectives. 1. 3D Reconstruction: Design and development of simple camera settings to capture real-time 3D reconstruction of the body sufficiently accurate to compute reliable balance/gait measurements. The system will consist in a single depth camera (e.g. Kinect from Microsoft) placed in front of the patient and two planar mirrors positioned behind providing “virtual” rear views to get a full 3D view of the subject during a balance or gait test. 2. Balance and Gait Analysis: Development of innovative algorithms in order to identify relevant clinical parameters from the 3D reconstructed body. To this end, machine learning and statistical analysis of shape and motion features will be used. 3. Validation: 3D data accuracy will be evaluated with 3D geometric object (e.g. cylinder) with known dimensions and with human mannequin previously scanned with a gold standard 3D imaging device (e.g. laser scanner or optical digitizer). Balance and gait analysis will be validated with student volunteers performing simulated balance impairment in laboratory. Correlations between our computed measurements and gold standard functional gait and balance tests will be performed to determine the ability of our methodology. This research will set the ground for the development of unique computer vision-based balance and gait analysis system for elderly healthcare professionals. It will contribute to improve clinical care and consequently the quality of life of elderly but also for patients suffering from other diseases or injuries affecting their balance and gait. We expect that this type of imaging systems will be used in clinics, for instance as a screening tool to detect elderly balance/gait problem early, before more sophisticated tests be conducted in specialized facilities. We hope that the relative simplicity of our system will facilitate balance/gait examinations for the elderly patient and encourage clinicians to use it.