Wearable sensors technology based on inertial measurement units (IMUs) is leading the transition from laboratory-based gait analysis, to daily life gait monitoring. However, the validity of IMU-based methods for the detection of gait events has only been tested in laboratory settings, which may not reproduce real life walking patterns. The aim of this study was to evaluate the accuracy of two algorithms for the detection of gait events and temporal parameters during free-living walking, one based on two shank-worn inertial sensors, and the other based on one waist-worn sensor. The algorithms were applied to gait data of ten healthy subjects walking both indoor and outdoor, and completing protocols that entailed both straight supervised and free walking in an urban environment. The values obtained from the inertial sensors were compared to pressure insoles data. The shank-based method showed very accurate initial contact, stride time and step time estimation (<14ms error). Accuracy of final contact timings and stance time was lower (28-51ms error range). The error of temporal parameter variability estimates was in the range 0.09-0.89%. The waist method failed to detect about 1% of the total steps and performed worse than the shank method, but the temporal parameter estimation was still satisfactory. Both methods showed negligible differences in their accuracy when the different experimental conditions were compared, which suggests their applicability in the analysis of free-living gait.

The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications.

Objective. The minimum clinically important difference (MCID) is a standard way of measuring clinical relevance. The objective of this work was to establish the MCID for the 6‐minute walking test (6minWT) and the Gross Motor Function Measure (GMFM‐88) in pediatric gait disorders. Methods. A cohort, pretest‐posttest study was conducted in a hospitalized care setting. A total of 182 patients with acquired brain injury (ABI) or cerebral palsy (CP) performed 20 robot‐assisted gait training sessions complemented with 20 sessions of physical therapy over 4 weeks. Separate MCIDs were calculated using 5 distribution‐based approaches, complemented with an anonymized survey completed by clinical professionals. Results. The MCID range for the 6minWT was 20‐38 m in the ABI cohort, with subgroup ranges of 20‐36 m for GMFCS I‐II, 23‐46 m for GMFCS III, and 24‐46 m for GMFCS IV. MCIDs for the CP population were 6‐23 m, with subgroup ranges of 4‐28 m for GMFCS I‐II, 9‐19 m for GMFCS III, and 10‐27 m for GMFCS IV. For GMFM‐88 total score, MCID values were 1.1%‐5.3% for the ABI cohort and 0.1%‐3.0% for the CP population. For dimension “D” of the GMFM, MCID ranges were 2.3%‐6.5% and 0.8%‐5.2% for ABI and CP populations, respectively. For dimension “E,” MCID ranges were 2.8%‐6.5% and 0.3%‐4.9% for ABI and CP cohorts, respectively. The survey showed a large interquartile range, but the results well mimicked the distribution‐based methods. Conclusions. This study identified for the first time MCID ranges for 6minWT and GMFM‐88 in pediatric patients with neurological impairments, offering useful insights for clinicians to evaluate the impact of treatments. Distribution‐based methods should be used with caution: methods based on pre‐post correlation may underestimate MCID when applied to patients with small improvements over the treatment period. Our results should be complemented with estimates obtained using consensus‐ and anchor‐based approaches.

Wearable sensors offer the potential to bring new knowledge to inform interventions in patients affected by multiple sclerosis (MS) by thoroughly quantifying gait characteristics and gait deficits from prolonged daily living measurements. The aim of this study was to characterise gait in both laboratory and daily life conditions for a group of patients with moderate to severe ambulatory impairment due to MS. To this purpose, algorithms to detect and characterise gait from wearable inertial sensors data were also validated. Fourteen patients with MS were divided into two groups according to their disability level (EDSS 6.5–6.0 and EDSS 5.5–5.0, respectively). They performed both intermittent and continuous walking bouts (WBs) in a gait laboratory wearing waist and shank mounted inertial sensors. An algorithm (W-CWT) to estimate gait events and temporal parameters (mean and variability values) using data recorded from the waist mounted sensor (Dynaport, Mc Roberts) was tested against a reference algorithm (S-REF) based on the shank-worn sensors (OPAL, APDM). Subsequently, the accuracy of another algorithm (W-PAM) to detect and classify WBs was also tested. The validated algorithms were then used to quantify gait characteristics during short (sWB, 5–50 steps), intermediate (iWB, 51–100 steps) and long (lWB, >100 steps) daily living WBs and laboratory walking. Group means were compared using a two-way ANOVA. W-CWT compared to S-REF showed good gait event accuracy (0.05–0.10 s absolute error) and was not influenced by disability level. It slightly overestimated stride time in intermittent walking (0.012 s) and overestimated highly variability of temporal parameters in both intermittent (17.5%–58.2%) and continuous walking (11.2%–76.7%). The accuracy of W-PAM was speed-dependent and decreased with increasing disability. The ANOVA analysis showed that patients walked at a slower pace in daily living than in the laboratory. In daily living gait, all mean temporal parameters decreased as the WB duration increased. In the sWB, the patients with a lower disability score showed, on average, lower values of the temporal parameters. Variability decreased as the WB duration increased. This study validated a method to quantify walking in real life in people with MS and showed how gait characteristics estimated from short walking bouts during daily living may be the most informative to quantify level of disability and effects of interventions in patients moderately affected by MS. The study provides a robust approach for the quantification of recognised clinically relevant outcomes and an innovative perspective in the study of real life walking.

The availability of wearable sensors allows shifting gait analysis from the traditional laboratory settings, to daily life conditions. However, limited knowledge is available about whether alterations associated to different testing environment (e.g. indoor or outdoor) and walking protocols (e.g. free or controlled), result from actual differences in the motor behaviour of the tested subjects or from the sensitivity to these changes of the indexes adopted for the assessment. In this context, it was hypothesized that testing environment and walking protocols would not modify motor control stability in the gait of young healthy adults, who have a mature and structured gait pattern, but rather the variability of their motor pattern. To test this hypothesis, data from trunk and shank inertial sensors were collected from 19 young healthy participants during four walking tasks in different environments (indoor and outdoor) and in both controlled (i.e. following a predefined straight path) and free conditions. Results confirmed what hypothesized: variability indexes (Standard deviation, Coefficient of variation and Poincaré plots) were significantly influenced by both environment and walking conditions. Stability indexes (Harmonic ratio, Short term Lyapunov exponents, Recurrence quantification analysis and Sample entropy), on the contrary, did not highlight any change in the motor control. In conclusion, this study highlighted an influence of environment and testing condition on the assessment of specific characteristics of gait (i.e. variability and stability). In particular, for young healthy adults, both environment and testing conditions affect gait variability indexes, whereas neither affect gait stability indexes.