Inertial measurement units (IMU) sensors are non-intrusive and pervasive available. In the past decades, researchers with diverse backgrounds were thrust into exploring wearable IMUs in health-related applications. As a fact of the aging population: The global population aged 60 years or over has doubled since 1980s, Canada is one of the nations facing the increasing senior population. It is projected over the next 20 years, Canada’s senior population is expected to grow by 68%, which brings new challenges to the public health service. Elder people are fragile and less mobile compared to young adults, thus, makes it important to monitor and assess their mobility status. Wearable IMU sensors offer a promising solution to this emerging challenge.
The objective of our project is to apply a deep learning approach on analyzing data collected from wearable IMU sensors and provide an accurate assessment based on the analyzed data for primary care providers of older people. We are also exploring some possible alternate data simulation techniques in order to provide more data for the machine learning model by simulating IMU from video or optical motion capture (MoCap) system.
Experiments demonstrated that IFLF is effective in handling both subject and device diversion across popular open datasets and an in-house dataset. It outperforms a baseline model up to 40% in test accuracy.
Therefore, we propose to simulate IMU data at arbitrary on-body locations from either MoCap or RGB video data. The simulated data can be further utilized for downstream tasks such as HAR, HPE and etc.
With accurate estimation of 3D knee joint gait kinematics with Deep Learning approach using IMUs and the optimal wearable inertial sensor placement combination, we can also remove the need for expensive and time-consuming collections and calibrations of the traditional kinematics estimation procedure. This work has the opportunity to help healthy adults, older adults, and clinical patients accurately monitor their motion outside the laboratory in a simple, low-cost, and comfortable way. This detailed and accurate kinematic information would allow for patients and doctors to monitor movement, promote health, and ultimately lighten the burden of musculoskeletal disorders on the national healthcare system.
We gratefully acknowledge the support of NSERC Discovery and CREATE programs with the funding for this
research, and extend our thanks to MIRA for the opportunity of interdisciplinary cooperation.
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