OBJECTIVE: Current methods of assessing the affect patients' health has on their daily lives are extremely limited. The aim of this paper is to develop a sensor-based approach to health status measurement in order to objectively measure health status. METHODS: Techniques to generate human behavior profiles, derived from the smartphone accelerometer and gyroscope sensors, are proposed. Experiments, using SVM regression models, are then conducted in order to evaluate the use of the proposed behavior profiles as a predictor of health status. RESULTS: Experiments were conducted on data from 171 participants, with an average of 114 h of data per participant. Regression models were trained and tested on the 10 SF-36 self-ratings. Results showed that the eight individual SF-36 scales and two component scores could be predicted with an average correlation of 0.683 and 0.698, respectively. General health was predicted with an average correlation of 0.752. CONCLUSION: Research shows that the clinically important difference for SF-36 self-ratings are approximately 10 points. Health status prediction errors in this study were 11.7 points on average. While the problem has not been fully solved, this paper presents a hugely promising direction for health status prediction. SIGNIFICANCE: Using the proposed techniques, health status could be measured using unobtrusive, inexpensive, and already available hardware. It could provide a means for clinicians to accurately and objectively assess the daily life benefits of treatments on an individual patient basis.
OBJECTIVE: Current methods of assessing the affect patients' health has on their daily lives are extremely limited. The aim of this paper is to develop a sensor-based approach to health status measurement in order to objectively measure health status. METHODS: Techniques to generate human behavior profiles, derived from the smartphone accelerometer and gyroscope sensors, are proposed. Experiments, using SVM regression models, are then conducted in order to evaluate the use of the proposed behavior profiles as a predictor of health status. RESULTS: Experiments were conducted on data from 171 participants, with an average of 114 h of data per participant. Regression models were trained and tested on the 10 SF-36 self-ratings. Results showed that the eight individual SF-36 scales and two component scores could be predicted with an average correlation of 0.683 and 0.698, respectively. General health was predicted with an average correlation of 0.752. CONCLUSION: Research shows that the clinically important difference for SF-36 self-ratings are approximately 10 points. Health status prediction errors in this study were 11.7 points on average. While the problem has not been fully solved, this paper presents a hugely promising direction for health status prediction. SIGNIFICANCE: Using the proposed techniques, health status could be measured using unobtrusive, inexpensive, and already available hardware. It could provide a means for clinicians to accurately and objectively assess the daily life benefits of treatments on an individual patient basis.
Authors: Samuel I Berchuck; Alessandro A Jammal; David Page; Tamara J Somers; Felipe A Medeiros Journal: Transl Vis Sci Technol Date: 2022-10-03 Impact factor: 3.048