Identifying Sensors-based Parameters Associated with Fall Risk in Community-Dwelling Older Adults: An Investigation and Interpretation of Discriminatory Parameters

Abstract

Background Falls pose a severe threat to the health of older adults worldwide. Determining gait and kinematic parameters that are related to an increased risk of falls is essential for developing effective intervention and fall prevention strategies. This study aimed to investigate the discriminatory parameter, which lay an important basis for developing effective clinical screening tools for identifying high-fall-risk older adults.Methods Forty-one individuals aged 65 years and above living in the community participated in this study. The older adults were classified as high-fall-risk and low-fall-risk individuals based on their BBS scores. The participants wore an inertial measurement unit (IMU) while conducting the Timed Up and Go (TUG) test. Simultaneously, a depth camera acquired images of the participants' movements during the experiment. After segmenting the data according to subtasks, 142 parameters were extracted from the sensor-based data. A t-test or Mann-Whitney U test was performed on the parameters for distinguishing older adults at high risk of falling. Multiple logistic regression was used to further quantify the role of different parameters in identifying high-fall-risk individuals.Results Fifteen participants were defined as high-fall-risk individuals, while twenty-six were defined as low-fall-risk individuals. 17 parameters were tested for significance with p-values less than 0.05. Multiple logistic regressions were constructed for the parameters with the p-values less than 0.005, including the coefficient of variation of the angular velocity in pitch (CV-Ang-Pitch) during the turn, usage of walking assistance, and the max and coefficient of variation of the angular velocity in yaw (Max-Ang-Yaw, CV-Ang-Yaw) during turn-to-sit. The results showed that the CV-Ang-Pitch during the turn was the parameter with the greatest effect on identifying high-fall-risk individuals.Conclusions High-fall-risk individuals typically exhibited a cautious gait, such as larger step width and shorter step length during walking. Besides, we identified some abnormal gait patterns of high-fall-risk individuals compared to low-fall-risk individuals, such as greater knee angle and a tendency to tilt the pelvis forward during turning. These findings provide insight into the mechanisms underlying the activities of high-fall-risk individuals compared to normal individuals and illustrate the role of sensors in identifying high-fall-risk individuals among community-dwelling older adults. (350 words)