Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study
Authors: Kluge Felix , Brand Yonatan E , Micó-Amigo M Encarna , Bertuletti Stefano , D'Ascanio Ilaria , Gazit Eran , Bonci Tecla , Kirk Cameron , Küderle Arne , Palmerini Luca , Paraschiv-Ionescu Anisoara , Salis Francesca , Soltani Abolfazl , Ullrich Martin , Alcock Lisa , Aminian Kamiar , Becker Clemens , Brown Philip , Buekers Joren , Carsin Anne-Elie , Caruso Marco , Caulfield Brian , Cereatti Andrea , Chiari Lorenzo , Echevarria Carlos , Eskofier Bjoern , Evers Jordi , Garcia-Aymerich Judith , Hache Tilo , Hansen Clint , Hausdorff Jeffrey M , Hiden Hugo , Hume Emily , Keogh Alison , Koch Sarah , Maetzler Walter , Megaritis Dimitrios , Niessen Martijn , Perlman Or , Schwickert Lars , Scott Kirsty , Sharrack Basil , Singleton David , Vereijken Beatrix , Vogiatzis Ioannis , Yarnall Alison , Rochester Lynn , Mazzà Claudia , Del Din Silvia , Mueller Arne 
Published: May 2024

Background

Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position 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 against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to 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), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to 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 ranging between 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 algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 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 relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups 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. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health.

Trial Registration

ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987

International Registered Report Identifier (IRRID)

RR2-10.1136/bmjopen-2021-050785