Estimating Walking Speed in the Wild

Abstract

An individual's physical activity substantially impacts the potential for prevention and recovery from diverse health issues, including cardiovascular diseases. Precise quantification of a patient's level of day-to-day physical activity, which can be characterized by the type, intensity, and duration of movement, is crucial for clinicians. Walking is a primary and fundamental physical activity for most individuals. Walking speed has been shown to correlate with various heart pathologies and overall function. As such, it is often used as a metric to assess health performance. A range of clinical walking tests exist to evaluate gait and inform clinical decision-making. However, these assessments are often short, provide qualitative movement assessments, and are performed in a clinical setting that is not representative of the real-world. Technological advancements in wearable sensing and associated algorithms enable new opportunities to complement in-clinic evaluations of movement during free-living. However, the use of wearable devices to inform clinical decisions presents several challenges, including lack of subject compliance and limited sensor battery life. To bridge the gap between free-living and clinical environments, we propose an approach in which we utilize different wearable sensors at different temporal scales and resolutions. Here, we present a method to accurately estimate gait speed in the free-living environment from a low-power, lightweight accelerometer-based bio-logging tag secured on the thigh. We use high-resolution measurements of gait kinematics to build subject-specific data-driven models to accurately map stride frequencies extracted from the bio-logging system to stride speeds. The model-based estimates of stride speed were evaluated using a long outdoor walk and compared to stride parameters calculated from a foot-worn inertial measurement unit using the zero-velocity update algorithm. The proposed method presents an average concordance correlation coefficient of 0.80 for all subjects, and 97% of the error is within ±0.2m· s−1. The approach presented here provides promising results that can enable clinicians to complement their existing assessments of activity level and fitness with measurements of movement duration and intensity (walking speed) extracted at a week time scale and in the patients' free-living environment.

Personalizing exoskeleton assistance while walking in the real world

“…People take thousands of steps per day, but real-world walking occurs in many separate bouts, most of which are short, with 90% being less than 100 steps in duration 34 . Speed is relatively consistent within each bout, but varies across bouts 27 . This fragmentation presents a challenge for collecting optimization data and efficiently fine-tuning assistance.…”

“…We developed a speed-adaptive controller to adjust assistance based on natural variations in walking speed. People vary their walking speed widely during the day 27 in response to changes in context 28 and constraints 29 . Variations in speed complicate exoskeleton control and may help explain why assistive devices that reduce walking effort during steady walking on a treadmill have not provided similar benefits during use under more natural conditions 17 .…”

“…5b and Supplementary Video 2 ). Participants were given ecologically relevant 35 audio prompts 28 that caused them to self-select walking speeds that matched a ground-truth distribution 27 (Fig. 5c ).…”

IMU-derived kinematics detect gait differences with age or knee osteoarthritis but differ from marker-derived inverse kinematics

“…Using trigger events, we split continuous IMU data into walking trials in MATLAB. We extracted one stride from each walking trial based on gait events identified using a one-dimensional continuous wavelet transform of the foot vertical acceleration data (Baroudi et al 2020; Hafer et al 2020). Finally, we used a Kalman filter to estimate sensor orientations in a global reference frame from raw linear acceleration, angular velocity, and magnetometer signals (Holmstrom 2016 Oct 26) and exported the IMU orientation data in quaternion format.…”

Gait metrics analysis utilizing single-point inertial measurement units: a systematic review

“…These methods have been implemented for single sensors placed on the trunk (85-87), waist (88,89), shank (90), ear (76) and foot (91,92). A thigh-based singlepoint IMU, the Activpal (PAL Technologies, Glasgow, Scotland), has also been used to measure mobility (73,93). However, this sensor has, to our knowledge, not analysed gait metrics beyond step identification in a single-point system.…”