Development of Wearable Gait Assistive Device Using Recurrent Neural Network
| Autor: | Chee-Kong Chui, C H Chua Matthew, Ngoc Son Hoang, Jeong Hoon Lim, Shi Yuan Tang |
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| Rok vydání: | 2019 |
| Předmět: |
medicine.medical_specialty
Rehabilitation Computer science medicine.medical_treatment Solution architecture Wearable computer 02 engineering and technology Accelerometer Data modeling 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Gait (human) Recurrent neural network Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing 030217 neurology & neurosurgery |
| Zdroj: | SII |
| Popis: | In elderly population, gait disorders are common where majority of these disorders are associated as symptoms of neurodegenerative diseases including Parkinson’s Disease (PD), Huntingtons Disease (HD), and Amyotrophic Lateral Sclerosis (ALS). In addition to affected mobility, the patients are also susceptible to greater risk of falls, hence increasing the demand for caretakers. With the trend of aging population, personal assistive device could be deployed to assist patients to regain independence and improve their quality of life. This paper proposes an end-to-end solution architecture for real-time standalone wearable gait assistive device to automate the rehabilitation activity. A key aspect of this study is to incorporate recurrent neural network (RNN) model that provides accurate pattern recognition and output actuation cue to the patients. Prototype and simulation data was used to show the feasibility of the proposed architecture and machine learning model. Preliminary results indicate favorable accuracy gait cycle detection for implementation. However, further optimizations are required to lower the computational costs and shorten the time lag between cycles to ensure low cost feasibility of the device. |
| Databáze: | OpenAIRE |
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