Elderly fall-risk prediction study from Inertial Sensor Time Series Data using Deep Learning Algorithms
碩士 === 元智大學 === 工業工程與管理學系 === 106 === Human fall risk prediction is a difficult task to accomplish. There has been numerous research done related to this problem attempting to predict a patient’s possible fall risk by manually extracting features from accelerometer sensor data. With the knowledge th...
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| Format: | Others |
| Language: | en_US |
| Published: |
2018
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| Online Access: | http://ndltd.ncl.edu.tw/handle/r426sz |
| Summary: | 碩士 === 元智大學 === 工業工程與管理學系 === 106 === Human fall risk prediction is a difficult task to accomplish. There has been numerous research done related to this problem attempting to predict a patient’s possible fall risk by manually extracting features from accelerometer sensor data. With the knowledge that deep learning algorithms can learn and predict successfully on time series data, this paper proposes a starting point for fall risk prediction by implementing and comparing an MLP, a CNN and a LSTM on time series data collected by an accelerometer sensor in a 3 meter time up and go test performed on a total of 150 elderly patients across different communities in Taiwan. Every file is labeled using the BBS score criteria where a patient with a score above 23 is labeled as healthy while a patient with a score less than 23 is labeled as fall risk. After training the networks, it becomes clear that BBS score criteria is far from ideal as the results from applying T-SNE reveals.
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