An Energy-Efficient Algorithm for Classification of Fall Types Using a Wearable Sensor

Objective: To mitigate damage from falls, it is essential to provide medical attention expeditiously. Many previous studies have focused on detecting falls and have shown that falls can be accurately detected at least in a laboratory setting. However, a very few studies have classified the different...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.31321-31329
Main Authors: Kwon, Soon Bin, Park, Jeong-Ho, Kwon, Chiheon, Kong, Hyung Joong, Hwang, Jae Youn, Kim, Hee Chan
Format: Article
Language:English
Subjects:
Acceleration
Accelerometers
Algorithms
Classification
Classification algorithms
Emergency medical services
Energy efficiency
Fall detection
fall type classification
Feature extraction
Frequency ranges
Inertial platforms
Inertial sensing devices
Machine learning
Machine learning algorithms
Physicians
Sampling
temporal signal angle measurement
wearable device
Wearable technology
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Summary:Objective: To mitigate damage from falls, it is essential to provide medical attention expeditiously. Many previous studies have focused on detecting falls and have shown that falls can be accurately detected at least in a laboratory setting. However, a very few studies have classified the different types of falls. To this end, in this paper, a novel energy-efficient algorithm that can discriminate the five most common fall types was developed for wearable systems. Methods: A wearable system with an inertial measurement unit sensor was first developed. Then, our novel algorithm, temporal signal angle measurement (TSAM), was used to classify the different types of falls at various sampling frequencies, and the results were compared with those from three different machine learning algorithms. Results: The overall performance of the TSAM and that of the machine learning algorithms were similar. However, the TSAM outperformed the machine learning algorithms at frequencies in the range of 10-20 Hz. As the sampling frequency dropped from 200 to 10Hz, the accuracy of the TSAM ranged from 93.3% to 91.8%. The sensitivity and specificity ranges from 93.3% to 91.8%, and 98.3% to 97.9%, respectively for the same frequency range. Conclusion: Our algorithm can be utilized with energy-efficient wearable devices at low sampling frequencies to classify different types of falls. Significance: Our system can expedite medical assistance in emergency situations caused by falls by providing the necessary information to medical doctors or clinicians.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2902718