With advancing age, gait disorders associated with stroke, Parkinson's disease, osteoarthritis, and falls increase. Gait analysis helps discriminate among different disorders, provide therapeutic recommendations, and monitor rehabilitative progress. Clinical gait assessment is limited to simple performance-based metrics. Gait laboratories provide formal, quantitative means, but are scarce, expensive, time-consuming, and unavailable in free-living settings. A simple, easy, inexpensive quantitative tool for clinicians, therapists and patients to screen for gait disorders, assess severity, and evaluate therapeutic effectiveness would be of significant benefit in managing the public health burden of gait disorders and falling. Basic research has shown that microelectronic accelerometers (ACC) can detect a wide variety of spatiotemporal gait variables indicative of gait disorder patterns. However, accelerometer research and products to date have two shortcomings: 1) they do not exploit information about stride waveform shape useful to detect stride cycle phases and accurately predict stride length and speed;and 2) they require post-processing of data by computer and cannot provide users with real-time information. The vision of Living Systems, Inc. (LSI) is to provide wearable, comfortable, attractive, inexpensive patient-accessible products integrated into the healthcare environment. These products will produce useful, valid and reliable characterizations of gait function in real time. We believe that realizing this vision is necessary to address the disease burden of gait dysfunction. To achieve our vision, we have developed a novel approach to ACC data processing, Stride-based ACC (SBA). SBA extracts steps from the time domain and treats each step as a mathematical object for modeling as a compact representation of duration, magnitude, and shape parameters. SBA overcomes the two shortcomings of accelerometers to date by 1) including shape in the analysis of the ACC waveform, and 2) using a compact data representation amenable to real-time processing. In Phase I, we will prove the feasibility of capturing relevant gait variables using SBA on unimpaired subjects. SBA will be applied to ACC waveforms and compared to gait lab motion analysis system (MAS) data. In Phase II, we will expand our application of SBA to impaired subjects and demonstrate real-time processing. Phase III will commercialize SBA in a miniature electronics device in conjunction with our industrial partners. LSI has assembled a multi-disciplinary team of staff, subcontractors and consultants with the necessary expertise in mathematics, accelerometry, biomechanics, medicine, and exercise physiology to achieve our technical goals. The significance of our overall SBIR effort is to make the benefits of evidence-based diagnosis, treatment and remediation as broadly available as possible to those affected by gait impairments associated with disease and risk of fall.
Gait disorders and the risk of falling increase with advancing age, diminish quality of life, and drive higher healthcare costs. We believe that the disease burden of gait dysfunction-which is expanding as our population ages-cannot be adequately addressed without providing real-time gait assessment directly to those affected. Therefore, the overall goal of the proposed multi-phase SBIR effort is to develop wearable, comfortable, attractive, inexpensive, patient-centered gait assessment information devices for direct use by those impacted by gait dysfunction (and in conjunction with their caregivers) in the normal, community-dwelling setting.
