title: Classifying step and spin turns using wireless gyroscopes and implications for fall risk assessments.
abstract: Recent studies have reported a greater prevalence of spin turns, which are more unstable than step turns, in older adults compared to young adults in laboratory settings. Currently, turning strategies can only be identified through visual observation, either in-person or through video. This paper presents two unique methods and their combination to remotely monitor turning behavior using three uniaxial gyroscopes. Five young adults performed 90° turns at slow, normal, and fast walking speeds around a variety of obstacles while instrumented with three IMUs (attached on the trunk, left and right shank). Raw data from 360 trials were analyzed. Compared to visual classification, the two IMU methods' sensitivity/specificity to detecting spin turns were 76.1%/76.7% and 76.1%/84.4%, respectively. When the two methods were combined, the IMU had an overall 86.8% sensitivity and 92.2% specificity, with 89.4%/100% sensitivity/specificity at slow speeds. This combined method can be implemented into wireless fall prevention systems and used to identify increased use of spin turns. This method allows for longitudinal monitoring of turning strategies and allows researchers to test for potential associations between the frequency of spin turns and clinically relevant outcomes (e.g., falls) in non-laboratory settings.
authors.affiliation: Department of Mechanical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. fino@vt.edu.
authors.forename: Peter C
authors.initials: PC
authors.lastname: Fino
authors.affiliation: Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA. cframes5@vt.edu.
authors.forename: Christopher W
authors.initials: CW
authors.lastname: Frames
authors.affiliation: School of Biological and Health Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ 85287, USA. Thurmon.lockhart@asu.edu.
authors.forename: Thurmon E
authors.initials: TE
authors.lastname: Lockhart
mesh_terms: D061725:Accelerometry; D000058:Accidental Falls; D000328:Adult; D004867:Equipment Design; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation / Q000379:methods; D018570:Risk Assessment / Q000379:methods*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D059015:Wireless Technology / Q000295:instrumentation*; D055815:Young Adult