Development and trialling of a low-power wearable fall detector

This thesis presents three generations of low-power fall detectors based on triaxial accelerometry and barometric pressure signals. The proposed fall detectors take the form of a pendant attached to a neck lanyard. Each generation incorporates multiple power-conserving technologies to achieve a long battery life, whilst remaining high fall detection accuracy. The prototypes devices were evaluated systematically using the data collected in several laboratory-based and free-living trials. The battery lives of the proposed fall detectors were estimated using the data from a series of benchtop electrical tests and free-living trials. The first generation of fall detector (Neon 1) achieved 93.0 % sensitivity and 87.3 % specificity, a false alarm rate of 0.023 false alarms/hour and a battery life of 664.9 days with a 3.7 V, 450 mAh battery. The second generation of fall detector was developed to overcome the limitations of Neon 1. Neon 2 with three variants of the fall detection algorithm, each employed a different pressure signal processing method (Method 1, Method 2, and Method 3) were investigated using the data collected from the human trials involving more volunteers than those in the development of Neon 1. Method 1, which mimicked the Neon 1, achieved a sensitivity of 91.5% and a specificity of 96.5%, a false alarm rate of 0.104 alarms/hour, and a battery life of 475 days with a 3.7 V, 450 mAh battery. Method 2 and Method 3 used a differential moving average filter and a Kalman filter for pressure signal processing, respectively. Method 2 achieved a sensitivity of 91.8% and a specificity of 95.2%, a false alarm rate of 0.035 alarms/hour, and a battery life of 1437 days with a 3.7 V, 450 mAh battery. Method 3 achieved a sensitivity of 91.9% and a specificity of 95.5%, a false alarm rate of 0.064 alarms/hour, and a battery life of 1428 days with a 3.7 V, 450 mAh battery. The Neon 2 was upgraded to the third generation of fall detector (Neon 2+) by implementing a smart triggering method of barometer. Method 3 based on a Kalman filter was chosen as the pressure signal processing method for the Neon 2+ because its responsiveness. The thresholds in the algorithm used in the Neon 2+ were trained with a novel optimisation method, which provided a set of compromise solutions to balance sensitivity and false alarm rate. According to the optimisation results, the Neon 2+ achieved a minimum false alarm rate of 0.058 alarms/hour with a sensitivity of 91.5%. The maximum sensitivity of the Neon 2+ was 94.5 % while the false alarm rate would increase to 0.603 alarms/hour. The estimated battery life for the Neon 2+ is 1135 days with a 3.7 V, 450 mAh battery.

Grantor dc:publisher

UNSW, Sydney

Year dc:date

2019

Author dc:creator

• Lu, Wei

• Fall detection
• Wearable device
• Low-power technology

Statement dc:rights

• open access
• CC BY-NC-ND 3.0
• free_to_read

Licence

• https://purl.org/coar/access_right/c_abf2
• https://creativecommons.org/licenses/by-nc-nd/3.0/au/

Language dc:language

EN

Identifier

https://doi.org/10.26190/unsworks/3680

OAI identifier oai:identifier

oai:unsworks.library.unsw.edu.au:1959.4/61759