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

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 distinguis...