Abstract
Falls are dangerous for the elderly population; therefore many fall detection systems have been developed. However, previous methods are bulky for elderly people or only use a single sensor to isolate falls from daily living activities, which makes a fall difficult to distinguish. In this paper, we present a cost-effective and easy-to-use portable fall-detection sensor and algorithm. Specifically, to detect human falls, we used a three-axis accelerator and a three-axis gyroscope in a mobile phone. We used the Fourier descriptor-based frequency analysis method to classify both normal and falling status. From the experimental results, the proposed method detects falling status with 96.14% accuracy.
Highlights
2 Materials and Method2.1 Data acquisitionSubjects either walked or fell down while holding a mobile phone in their hand
The performance ind ices were evaluated by five performance indices, including classificat ion accuracy (TP+TN)/(TP+TN+FP+FN), sensitivity (TP/[TP+FN]), specificity (TN/[TN+FP]), positive predict ive value (TP/[TP+FP]), and negative predictive value (TN/[TN+FN]), where TP is the number of t rue positive findings correctly classified as positive; TN = true negative; FP = false positive; and FN = false negative [8]
The statistical analysis shows that the resulting accuracy was 96.14%, sensitivity was 100%, specificity was 92.08%, positive predictive value was 92.98%, and negative predictive value was 100%
Summary
Subjects either walked or fell down while holding a mobile phone in their hand. A three-axis accelero meter was used to detect users’ activity such as walking, falling, sitting, etc. One’s body rotates on the body’s center. A gyroscope that measures angular mo mentu m was used to detect this rotation. The sampling frequency of each data was 20 Hz. The device used for the data acquisition was the Galaxy Note 1 (Samsung Electronics, Seoul, South Korea). The total data set of walking and falling was 212 and 202, respectively. The mean age of the subjects was 23.2; the age range was from 21 to 24
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
