Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven controllers on locomotion assistive devices that recognize/predict the current locomotion mode or the upcoming one. This review synthesizes the machine learning algorithms designed to recognize or to predict a locomotion mode in order to automatically adapt the behavior of a locomotion assistive device. A systematic review was conducted on the Web of Science and MEDLINE databases (as well as in the retrieved papers) to identify articles published between 1 January 2000 to 31 July 2020. This systematic review is reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and is registered on Prospero (CRD42020149352). Study characteristics, sensors and algorithms used, accuracy and robustness were also summarized. In total, 1343 records were identified and 58 studies were included in this review. The experimental condition which was most often investigated was level ground walking along with stair and ramp ascent/descent activities. The machine learning algorithms implemented in the included studies reached global mean accuracies of around 90%. However, the robustness of those algorithms seems to be more broadly evaluated, notably, in everyday life. We also propose some guidelines for homogenizing future reports.

This pragmatic, multicenter, open-label, randomized controlled trial (RCT) aimed to compare the effectiveness, safety, and cost-utility of a custom-made knee brace versus usual care over 1 year in medial knee osteoarthritis (OA). 120 patients with medial knee OA (VAS pain at rest >40/100), classified as Kellgren-Lawrence grade II-IV, were randomized into two groups: ODRA plus usual care (ODRA group) and usual care alone (UCA group). The primary effectiveness outcome was the change in VAS pain between M0 and M12. Secondary outcomes included changes over 1 year in KOOS (function) and OAKHQOL (quality of life) scores. Drug consumption, compliance, safety of the knee brace, and cost-utility over 1 year were also assessed. The ODRA group was associated with a higher improvement in: VAS pain (adjusted mean difference of -11.8; 95% CI: -21.1 to -2.5); all KOOS subscales (pain: +8.8; 95% CI: 1.4-16.2); other symptoms (+10.4; 95% CI: 2.7-18); function in activities of daily living (+9.2; 95% CI: 1.1-17.2); function in sports and leisure (+12.3; 95% CI: 4.3-20.3); quality of life (+9.9; 95% CI: 0.9-15.9), OAKHQOL subscales (pain: +14.8; 95% CI: 5.0-24.6); and physical activities (+8.2; 95% CI: 0.6-15.8), and with a significant decrease in analgesics consumption at M12 compared with the UCA group. Despite localized side-effects, observance was good at M12 (median: 5.3 hours/day). The ODRA group had a more than 85% chance of being cost-effective for a willingness-to-pay threshold of €45 000 per QALY. The ERGONOMIE RCT demonstrated significant clinical benefits of an unloader custom-made knee brace in terms of improvements in pain, function, and some aspects of quality of life over 1 year in medial knee OA, as well as its potential cost-utility from a societal perspective.

Wireless wearable insoles are interesting tools to collect gait parameters during daily life activities. However, studies have to be performed specifically for each type of insoles on a big data set to validate the measurement in ecological situations. This study aims to assess the criterion validity and test-retest reliability of gait parameters from wearable insoles compared to motion capture system. Gait of 30 healthy participants was recorded using DSPro^® insoles and a motion capture system during overground and treadmill walking at three different speeds. Criterion validity and test-retest reliability of spatio-temporal parameters were estimated with an intraclass correlation coefficient (ICC). For both systems, reliability was found higher than 0.70 for all variables (p < 0.001) except for minimum toe clearance (ICC < 0.50) with motion capture system during overground walking. Regardless of speed and condition of walking, Speed, Cadence, Stride Length, Stride Time and Stance Time variables were validated (ICC > 0.90; p < 0.001). During walking on treadmill, loading time was not validated during slow speed (ICC < 0.70). This study highlights good criterion validity and test-retest reliability of spatiotemporal gait parameters measurement using wearable insoles and opens a new possibility to improve care management of patients using clinical gait analysis in daily life activities.

Used on clinical and sportive context, three-dimensional motion analysis is considered as the gold standard in the biomechanics field. The proposed dataset has been established on 30 asymptomatic young participants. Volunteers were asked to walk at slow, comfortable and fast speeds, and to run at comfortable and fast speeds on overground and treadmill using shoes. Three dimensional trajectories of 63 reflective markers, 3D ground reaction forces and moments were simultaneously recorded. A total of 4840 and 18159 gait cycles were measured for overground and treadmill walking, respectively. Additionally, 2931 and 18945 cycles were measured for overground and treadmill running, respectively. The dataset is presented in C3D and CSV files either in raw or pre-processed format. The aim of this dataset is to provide a complete set of data that will help for the gait characterization during clinical gait analysis and in a sportive context. This data could be used for the creation of a baseline database for clinical purposes to research activities exploring the gait and the run.

Sagittal balance, commonly impaired in lumbar spinal stenosis (LSS) patients, is typically assessed using the sagittal vertical axis (SVA) with EOS imaging. However, to limit X-ray exposure and medical costs, it could be interesting to evaluate the capacity of quantified motion analysis to estimate the 3D modeling of SVA in patients with symptomatic LSS. An estimation of the SVA in patients with LSS was performed with 3D motion analysis. SVA and “C7_PSI” (orthogonal horizontal distance between the vertical lines through the markers of the C7 vertebra and the middle of the posterosuperior iliac spine) were measured on 37 LSS patients using EOS radiography and postural and 3D motion analysis, respectively. Multiple stepwise linear regressions were performed with EOS SVA according to age, body mass index, C7_PSI and/or postural variables. A highly significant relationship was found between SVA and C7_PSI, mediolateral amplitude of CoP displacements and age (adjusted R²=0.69, p < 0.0001). While the postural analysis did not reveal significant relationships, the model using 3D parameters revealed significant relationships between radiographic SVA and C7_PSI and age (adjusted R²=0.65, p < 0.0001). 3D motion parameters with or without postural parameters may explain more than 65% of the variance seen in EOS imaging performed on LSS patients. These promising results in LSS patients suggest that the estimation of SVA with 3D motion analysis offers an alternative to EOS. In addition, SVA could be assessed at rest and during dynamic tasks. This study has been published in Clinical Trial registration (reference NCT03194607).