A Patient-Specific Single Sensor IoT-Based Wearable Fall Prediction and Detection System

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 27; no. 5; pp. 995 - 1003
• Main Authors: Saadeh, Wala, Butt, Saad Adnan, Altaf, Muhammad Awais Bin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Accelerometers; Accelerometry; Accidental Falls - prevention & control; Activities of daily living; Adult; Aged; Aged, 80 and over; Algorithms; Fall detection; fall prediction; Falls; Feature extraction; Female; gait monitoring; Health care; Humans; Injury prevention; Internet; Latency; Linear Models; Male; Monitoring, Ambulatory; Morbidity; Older people; patient-specific; Predictions; Reproducibility of Results; Senior citizens; Sensitivity; Sensitivity and Specificity; Support Vector Machine; Support vector machines; Thigh; threshold detection; Wearable Electronic Devices; wearable sensor; Wearable sensors
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2019.2911602

Falls in older adults are a major cause of morbidity and mortality and are a key class of preventable injuries. This paper presents a patient-specific (PS) fall prediction and detection prototype system that utilizes a single tri-axial accelerometer attached to the patient's thigh to distinguish between activities of daily living (ADL) and fall events. The proposed system consists of two modes of operation: 1) fast mode for fall predication (FMFP) predicting a fall event (300-700 msec) before occurring and 2) slow mode for fall detection (SMFD) with a 1-sec latency for detecting a fall event. The nonlinear support vector machine classifier (NLSVM)-based FMFP algorithm extracts seven discriminating features for the pre-fall case to identify a fall risk event and alarm the patient. The proposed SMFD algorithm utilizes a Three-cascaded 1-sec sliding frames classification architecture with a linear regression-based offline training to identify a single and optimal threshold for each patient. Fall incidence will trigger an alarming notice to the concern healthcare providers via the Internet. Experiments are performed with 20 different subjects (age above 65 years) and a total number of 100 associated falls and ADL recordings indoors and outdoors. The accuracy of the proposed algorithms is furthermore validated via MobiFall Dataset. FMFP achieves sensitivity and specificity of 97.8% and 99.1%, respectively, while SMFD achieves sensitivity and specificity of 98.6% and 99.3%, respectively, for a total number of 600 measured falls and ADL cases from 77 subjects.