Detecting free-living steps and walking bouts: validating an algorithm for macro gait analysis

Abstract

Research suggests wearables and not instrumented walkways are better suited to quantify gait outcomes in clinic and free-living environments, providing a more comprehensive overview of walking due to continuous monitoring. Numerous validation studies in controlled settings exist, but few have examined the validity of wearables and associated algorithms for identifying and quantifying step counts and walking bouts in uncontrolled (free-living) environments. Studies which have examined free-living step and bout count validity found limited agreement due to variations in walking speed, changing terrain or task. Here we present a gait segmentation algorithm to define free-living step count and walking bouts from an open-source, high-resolution, accelerometer-based wearable (AX3, Axivity). Ten healthy participants (20-33 years) wore two portable gait measurement systems; a wearable accelerometer on the lower-back and a wearable body-mounted camera (GoPro HERO) on the chest, for 1 h on two separate occasions (24 h apart) during free-living activities. Step count and walking bouts were derived for both measurement systems and compared. For all participants during a total of almost 20 h of uncontrolled and unscripted free-living activity data, excellent relative (rho ⩾ 0.941) and absolute (ICC ⩾ 0.975) agreement with no presence of bias were identified for step count compared to the camera (gold standard reference). Walking bout identification showed excellent relative (rho ⩾ 0.909) and absolute agreement (ICC ⩾ 0.941) but demonstrated significant bias. The algorithm employed for identifying and quantifying steps and bouts from a single wearable accelerometer worn on the lower-back has been demonstrated to be valid and could be used for pragmatic gait analysis in prolonged uncontrolled free-living environments.

Concern about Falling and Complexity of Free-Living Physical Activity Patterns in Well-Functioning Older Adults

“…Performance was characterized by the cadence of the longest locomotion period, and the number of locomotion bouts longer than 30 steps with cadence equal or superior to 100 steps/min, that may correspond to outdoor purposeful activity [21][22][23] (expressed as % of the total number of locomotion bouts).…”

“…The possible range of values spanned by each parameter was partitioned into several intervals, and combinations across intervals were related to 25 PA states. The fine-grained PA states corresponded to different levels of movement intensity during non-locomotion, classified according to the values of dynamic component of trunk acceleration, and to different locomotion intensity categorized according to the duration and cadence of each detected period [22,[25][26][27] (see online suppl. material for detailed description).…”

Wearables for independent living in older adults: Gait and falls

“…Thus, large portions of data may be excluded for micro analysis at low resolutions. Therefore, a methodology to quantify macro gait at a higher resolution (2.5-seconds) has been proposed, validated with a video recorder during extended periods of free-living [45] (Table 1) and subsequently used to compare micro gait in the clinic to free-living [38].…”

Observational Study of a Wearable Sensor and Smartphone Application Supporting Unsupervised Exercises to Assess Pain and Stiffness

“…This study has allowed us to collect data not only during 5×STS tests, but also during everyday activities such as morning routines, sleep, and commuting to work. Data collected during everyday activities have previously been used to estimate the risk of adverse events [34], or specific activities such as walking [35]. However, the relationship between sensor data collected during normal activities and qualitative health metrics, such as pain suffered during such activities, would benefit from a broader coverage.…”