Smartphone-Based Assessment of Gait During Straight Walking, Turning, and Walking Speed Modulation in Laboratory and Free-Living Environments

Silsupadol, Patima; Prupetkaew, Paphawee; Kamnardsiri, Teerawat; Lugade, Vipul

doi:10.1109/jbhi.2019.2930091

Cited by 27 publications

(35 citation statements)

References 24 publications

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“…Regarding gait outcomes, similar to previous findings reported by Silsupadol and colleagues [ 34 , 59 ], spatiotemporal gait parameters such as average step time, average step length, and walking speed showed moderate-to-excellent consistency and absolute agreement. These results reinforced that, through appropriate algorithms, inertial sensors embedded in current smartphones can provide results as valid as those displayed by inertial measurement units typically designed for research [ 25 ].…”

Section: Discussionsupporting

confidence: 87%

“…While assessing the validity of some spatiotemporal gait parameters was successful, this was not the case for step length/time variability and for step length/time asymmetry. These poor results verified the difficulty of capturing step-to-step variations using a single sensor placed on the low back, even though the gait tasks were performed in a straight line and not in a free-living environment [ 59 ]. A possible explanation for these results is the signal noise caused by the integration procedure applied as part of the inverted pendulum gait model used for obtaining these parameters from acceleration time series [ 43 ].…”

Section: Discussionmentioning

confidence: 92%

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Validity and Reliability of a Smartphone App for Gait and Balance Assessment

Rashid

Barbado

Olsen

et al. 2021

Sensors

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Advances in technology provide an opportunity to enhance the accuracy of gait and balance assessment, improving the diagnosis and rehabilitation processes for people with acute or chronic health conditions. This study investigated the validity and reliability of a smartphone-based application to measure postural stability and spatiotemporal aspects of gait during four static balance and two gait tasks. Thirty healthy participants (aged 20–69 years) performed the following tasks: (1) standing on a firm surface with eyes opened, (2) standing on a firm surface with eyes closed, (3) standing on a compliant surface with eyes open, (4) standing on a compliant surface with eyes closed, (5) walking in a straight line, and (6) walking in a straight line while turning their head from side to side. During these tasks, the app quantified the participants’ postural stability and spatiotemporal gait parameters. The concurrent validity of the smartphone app with respect to a 3D motion capture system was evaluated using partial Pearson’s correlations (rp) and limits of the agreement (LoA%). The within-session test–retest reliability over three repeated measures was assessed with the intraclass correlation coefficient (ICC) and the standard error of measurement (SEM). One-way repeated measures analyses of variance (ANOVAs) were used to evaluate responsiveness to differences across tasks and repetitions. Periodicity index, step length, step time, and walking speed during the gait tasks and postural stability outcomes during the static tasks showed moderate-to-excellent validity (0.55 ≤ rp ≤ 0.98; 3% ≤ LoA% ≤ 12%) and reliability scores (0.52 ≤ ICC ≤ 0.92; 1% ≤ SEM% ≤ 6%) when the repetition effect was removed. Conversely, step variability and asymmetry parameters during both gait tasks generally showed poor validity and reliability except step length asymmetry, which showed moderate reliability (0.53 ≤ ICC ≤ 0.62) in both tasks when the repetition effect was removed. Postural stability and spatiotemporal gait parameters were found responsive (p < 0.05) to differences across tasks and test repetitions. Along with sound clinical judgement, the app can potentially be used in clinical practice to detect gait and balance impairments and track the effectiveness of rehabilitation programs. Further evaluation and refinement of the app in people with significant gait and balance deficits is needed.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 92%

Validity and Reliability of a Smartphone App for Gait and Balance Assessment

Rashid

Barbado

Olsen

et al. 2021

Sensors

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“…In healthy adults, the values of the step duration and step length recorded with both the glasses and the optoelectronic system were in agreement with the values previously observed in the literature when measured with wearable sensors [ 37 , 38 , 39 ]. For example, our step duration values observed on the treadmill at the highest speed were similar to those previously measured with an accelerometer located at the lumbar level in young adults during level walking at a self-selected pace of ~1.4 m/s [ 39 ].…”

Section: Discussionsupporting

confidence: 89%

Smart Eyeglasses: A Valid and Reliable Device to Assess Spatiotemporal Parameters during Gait

Hellec

Chorin

Castagnetti

et al. 2022

Sensors

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The study aims to determine the validity and reproducibility of step duration and step length parameters measured during walking in healthy participants using an accelerometer embedded in smart eyeglasses. Twenty young volunteers participated in two identical sessions comprising a 30 s gait assessment performed at three different treadmill speeds under two conditions (i.e., with and without a cervical collar). Spatiotemporal parameters (i.e., step duration and step length normalized by the lower limb length) were obtained with both the accelerometer embedded in smart eyeglasses and an optoelectronic system. The relative intra- and inter-session reliability of step duration and step length computed from the vertical acceleration data were excellent for all experimental conditions. An excellent absolute reliability was observed for the eyeglasses for all conditions and concurrent validity between systems was observed. An accelerometer incorporated in smart eyeglasses is accurate to measure step duration and step length during gait.

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“…Gait analysis typically starts with detecting these local minima and maxima points to differentiate between right and left steps and to extract initial and final foot contact time points; in this study, we will refer to them as heel strike and toe offs, respectively. Temporal gait parameters are calculated from heel strike and toe off time points [16], [19]. In addition, it is possible to estimate step length indirectly using biomechanical models [20].…”

Section: A Related Workmentioning

confidence: 99%

“…Silsupadol et al [19] studied unsteady walking in 24 healthy participants while turning, accelerating and decelerating both in laboratory and outside environments. Although the authors were able to estimate velocity, step length, step time and cadence with high accuracy (Pearson correlation coefficient varied between 0.5 and 1), the accuracy of their gait symmetry estimations was reduced significantly (correlation coefficient < 0.5).…”

Section: A Related Workmentioning

confidence: 99%