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Clinical Trial

Clinical Trial

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Clinical Trial

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  1. Falls Detection using Accelerometry and Barometric Pressure 10 Author: Tabish Rizvi Supervisor: Dr. Stephen Redmond Motivation Falls and falls induced injuries among the elderly are a major area of concern. This is because the injuries that the elderly sustain due to falls often require immediate medical attention. To facilitate independent living, wearable sensor devices have been developed to automate falls detection. However, due to lack of available sensor data and/or poor algorithm design, falls detection devices currently suffer from high levels of inaccuracy. Objective The aim of this thesis was to improve falls classification by designing a falls detection algorithm that considers data from a tri-axial accelerometer, a tri-axial gyroscope and a barometer. Pattern Classification To serve as a point of comparison to decision tree classification, a Bayesian pattern classifier was adopted. A 0.25s time window was deemed to be sufficient to capture the information of a fall. Tri-axial Accelerometry Signals Acceleration (g) { Fall Time (s) At each 0.25s interval in time, the posterior probability is calculated for the feature vector using Bayes theorem: Feature Extraction From the sensor signals, features of interest such as the subject’s orientation are extracted to aid in falls detection. Subject Orientation A window in time is then classified as a fall if: Orientation (degrees) Clinical Trial To compare the accuracy of the falls algorithms, a clinical trial was conducted in a supervised environment with the University of New South Wales Ethics Committee approval. Five healthy volunteers (3 male and 0 female; age: 22.3 ± 0.57 years; height: 1.81 ± 0.04m) participated in the study. Subjects were asked to perform a sequence of falls and a series of actions that were designed to mimic activities of everyday living, e.g. sitting down into a chair. Time (s) Decision Tree Classification As a first approach to designing a falls detection algorithm, a decision tree classifier was considered. The algorithm is designed so as to minimise redundant computations and maximise detection accuracy. Results Yes Wait 1 sec ΔP > th? Mean tilt angle > 20⁰? Calculate mean tilt angle (1 sec) No Yes No Abnormal acc. peak? • ΔP peak? Yes Yes Wait 0.5 sec No Conclusion Algorithm 3 offers the best performance balance in terms of distinguishing fall events from movements of everyday living. The use of gyroscopes has aided in improving the accuracy of falls classification. Max tilt angle > 40⁰? Abnormal gyro. peak? Yes Gyro peak between range? Yes Tilt angle < 30⁰? • Future Work • Comprehensive falls trials. • Algorithm 3 gyroscope parameter adjustment. • Bayes classifier window size and feature vector optimisation. • Further application of pattern recognition techniques. Yes No Angular rotation mag. > 50⁰? aSMA < th? Yes Yes Yes Fall with recovery Fall