Validity of the Walked Distance Estimated by Wearable Devices in Stroke Individuals

Abstract

Background: Health professionals need valid devices to assess a stroke individual’s ability to walk. The aim was to evaluate the validity of the estimation of the walked distance by wearable devices and the impact of the sensor’s position in stroke individuals. Methods: Post-stroke patients able to walk without human assistance were equipped with several wearable devices: pedometers, Actigraph, and Sensewear Armband placed according to the manufacturers' recommendations. Participants walked for 6 min at a comfortable speed wearing all sensors at the same time. We analyzed the validity of sensor-estimated distances according to their position using Bland–Altman analysis, root-mean-square error, and coefficient of correlation. Results: In total, 35 individuals were included (mean age = 65 ± 15 years). The best estimations were given by the Actigraph worn on the unaffected ankle (mean bias (MB) = 22.6 ± 32.4 m; p = 0.37) and by the pedometer worn on the unaffected hip (MB = 20.5 ± 24.6 m; p = 0.46). The other sensors and positions provided large estimation errors over 95 m (p < 0.05). Conclusion: This study led to a recommendation of a pedometer worn on the unaffected hip or an Actigraph worn on the unaffected ankle to get a valid estimation of the distance walked by stroke individuals.

Validity of the International Physical Activity Questionnaire Long Form for Assessing Physical Activity and Sedentary Behavior in Subjects with Chronic Stroke

“…However, accelerometry depends on the acceleration of the body movement. In this sense, it is frequent that people post-stroke show low walking speed (<0.8 m/s), which consequently may be registered as LPA by the accelerometer since body acceleration may not meet the threshold for MVPA [ 60 ]. In this regard, subjects with low walking speed represented 44.6% of our sample.…”

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

“…Therefore, in Table 1, we only report the ranges of acquisition times that go from hours to years. Among the selected studies, as displayed in Figure 3 [40,43,45,48,49,[51][52][53]62,64,[67][68][69]87,91,95,99,102], 8% (N = 6) focused on aging and associated pathologies [38,56,66,88,91,100], and 4% (N = 3) focused on diseases associated with poor lifestyle [42,62,74]. Finally, five studies were classified as "others" [41,46,82,84,93] because they could not be grouped together in an existing group.…”

“…[35][36][37][38][39]41,44,46,47,50,51,53,54,56,58,59,64,66,71,[75][76][77][78][79]81,[83][84][85][86][90][91][92][93][94]96,97,[101][102][103], others use annotations made by experts on data from videos or measurements during the experiment[37,38,40,43,48,52,55,63,67,70,74,80,…”

Quantified Self-Using Consumer Wearable Device: Predicting Physical and Mental Health

“…Compagnat et al [33] 2019 Validate the reliability of wearable device accuracy and measure the impact of various wearable positions in stroke patients.…”

“…The rapid advancement of wearable device technology provides an opportunity for physicians and researchers to design and monitor good treatments for patients. For instance, Compagnat et al [33] recently published an exploratory research report on the utilization of wearable device in monitoring the walking habits of post-stroke patients. They recruited 35 individuals who had history of a single stroke that had been confirmed by brain imaging, the ability to walk for six minutes without the support of others, and no decompensated chronic pathologies.…”