Assessment of Foot Strike Angle and Forward Propulsion with Wearable Sensors in People with Stroke.

Effective retraining of foot elevation and forward propulsion is a critical aspect of gait rehabilitation therapy after stroke, but valuable feedback to enhance these functions is often absent during home-based training. To enable feedback at home, this study assesses the validity of an inertial measurement unit (IMU) to measure the foot strike angle (FSA), and explores eight different kinematic parameters as potential indicators for forward propulsion. Twelve people with stroke performed walking trials while equipped with five IMUs and markers for optical motion analysis (the gold standard). The validity of the IMU-based FSA was assessed via Bland-Altman analysis, ICC, and the repeatability coefficient. Eight different kinematic parameters were compared to the forward propulsion via Pearson correlation. Analyses were performed on a stride-by-stride level and within-subject level. On a stride-by-stride level, the mean difference between the IMU-based FSA and OMCS-based FSA was 1.4 (95% confidence: -3.0; 5.9) degrees, with ICC = 0.97, and a repeatability coefficient of 5.3 degrees. The mean difference for the within-subject analysis was 1.5 (95% confidence: -1.0; 3.9) degrees, with a mean repeatability coefficient of 3.1 (SD: 2.0) degrees. Pearson's r value for all the studied parameters with forward propulsion were below 0.75 for the within-subject analysis, while on a stride-by-stride level the foot angle upon terminal contact and maximum foot angular velocity could be indicative for the peak forward propulsion. In conclusion, the FSA can accurately be assessed with an IMU on the foot in people with stroke during regular walking. However, no suitable kinematic indicator for forward propulsion was identified based on foot and shank movement that could be used for feedback in people with stroke.

Validation of an algorithm to assess regular and irregular gait using inertial sensors in healthy and stroke individuals.

Background: Studies using inertial measurement units (IMUs) for gait assessment have shown promising results regarding accuracy of gait event detection and spatiotemporal parameters. However, performance of such algorithms is challenged in irregular walking patterns, such as in individuals with gait deficits. Based on the literature, we developed an algorithm to detect initial contact (IC) and terminal contact (TC) and calculate spatiotemporal gait parameters. We evaluated the validity of this algorithm for regular and irregular gait patterns against a 3D optical motion capture system (OMCS). Methods: Twenty healthy participants (aged 59 ± 12 years) and 10 people in the chronic phase after stroke (aged 61 ± 11 years) were equipped with 4 IMUs: on both feet, sternum and lower back (MTw Awinda, Xsens) and 26 reflective makers. Participants walked on an instrumented treadmill for 2 minutes (i) with their preferred stride lengths and (ii) once with irregular stride lengths (±20% deviation) induced by light projected stepping stones. Accuracy of the algorithm was evaluated on stride-by-stride agreement of IC, TC, stride time, length and velocity with OMCS. Bland-Altman-like plots were made for the spatiotemporal parameters, while differences in detection of IC and TC time instances were shown in histogram plots. Performance of the algorithm was compared between regular and irregular gait with a linear mixed model. This was done by comparing the performance in healthy participants in the regular vs irregular walking condition, and by comparing the agreement in healthy participants with stroke participants in the regular walking condition. Results: For each condition at least 1,500 strides were included for analysis. Compared to OMCS, IMU-based IC detection in both groups and condition was on average 9-17 (SD ranging from 7 to 35) ms, while IMU-based TC was on average 15-24 (SD ranging from 12 to 35) ms earlier. When comparing regular and irregular gait in healthy participants, the difference between methods was 2.5 ms higher for IC, 3.4 ms lower for TC, 0.3 cm lower for stride length, and 0.4 cm/s higher for stride velocity in the irregular walking condition. No difference was found on stride time. When comparing the differences between methods between healthy and stroke participants, the difference between methods was 7.6 ms lower for IC, 3.8 cm lower for stride length, and 3.4 cm/s lower for stride velocity in stroke participants. No differences were found on differences between methods on TC detection and stride time between stroke and healthy participants. Conclusions: Small irrelevant differences were found on gait event detection and spatiotemporal parameters due to irregular walking by imposing irregular stride lengths or pathological (stroke) gait. Furthermore, IMUs seem equally good compared to OMCS to assess gait variability based on stride time, but less accurate based on stride length.

The Influence of Stride Selection on Gait Parameters Collected with Inertial Sensors.

Different methods exist to select strides that represent preferred, steady-state gait. The aim of this study was to identify the effect of different stride-selection methods on spatiotemporal gait parameters to analyze steady-state gait. A total of 191 patients with hip or knee osteoarthritis (aged 38-85) wearing inertial sensors walked back and forth over 10 m for two minutes. After the removal of strides in turns, five stride-selection methods were compared: (ALL) include all strides, others removed (REFERENCE) two strides around turns, (ONE) one stride around turns, (LENGTH) strides <63% of median stride length, and (SPEED) strides that fall outside the 95% confidence interval of gait speed over the strides included in REFERENCE. Means and SDs of gait parameters were compared for each trial against the most conservative definition (REFERENCE). ONE and SPEED definitions resulted in similar means and SDs compared to REFERENCE, while ALL and LENGTH definitions resulted in substantially higher SDs of all gait parameters. An in-depth analysis of individual strides showed that the first two strides after and last two strides before a turn were significantly different from steady-state walking. Therefore, it is suggested to exclude the first two strides around turns to assess steady-state gait.

Translation and validation of the System Usability Scale to a Dutch version: D-SUS.

PURPOSE: The System Usability Scale (SUS) is the most commonly used questionnaire to assess usability of healthcare innovations but is not available in Dutch (D-SUS). This study aims to translate the SUS to Dutch and to determine its internal consistency, test-retest reliability, and construct validity in healthcare innovations focused on rehabilitation technologies. METHODS: Translation of the SUS was performed according to the WHO recommendations. Fifty-four participants filled out the D-SUS and Dutch Quebec User Evaluation of Satisfaction with assistive Technology (D-QUEST) twice. Internal consistency was assessed by Cronbach's alpha. Test-retest reliability was evaluated by Gwet's agreement coefficient (Gwet's AC2) on item scale, and Pearson correlation coefficient (PCC) for the overall D-SUS scores. Construct validity was assessed with the PCC between the D-SUS and D-QUEST overall scores (Netherlands Trial Register, ID: NL9169). RESULTS: After translation, Cronbach's alpha was 0.74. Gwet's AC2 was 0.68 and the PCC between the first and second overall D-SUS scores was 0.75. No significant difference in D-SUS score between the two measurements was found. Repeatability coefficient was 18.4. The PCC between the D-SUS and D-QUEST overall scores was 0.49. CONCLUSIONS: The D-SUS is a valid and reliable tool for usability assessment of healthcare innovations, specifically rehabilitation technologies.

Successful implementation of new rehabilitation technologies is partially dependent on good system usability.The System Usability Scale is translated to Dutch (D-SUS) to evaluate usability of healthcare innovations in the Netherlands.The D-SUS is a reliable and valid method to measure usability of rehabilitation technologies and eHealth applications.