Real-World Gait Detection Using a Wrist-Worn Inertial Sensor:Validation Study

Background: Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Precedingthe estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regionsof interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms existfor other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets acrossdifferent disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower backposition have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. Objective: The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position againstreference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms appliedto the wrist position to those applied to lower back-worn inertial sensors. Methods: Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronicobstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in thereal-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors asreference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and comparedto gait detection performance using lower back-worn inertial sensors. Results: The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity rangingbetween 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98(SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%)per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithmapplied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivitybetween 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relativeabsolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in diseasegroups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. Conclusions: Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Thoseperiods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait durationdistribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinicalstudies and public health. Trial Registration: ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN1224698 7International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2021-050785