Measuring Kinematic Response to Perturbed Locomotion in Young Adults

Daily life activities often require humans to perform locomotion in challenging scenarios. In this context, this study aimed at investigating the effects induced by anterior-posterior (AP) and medio-lateral (ML) perturbations on walking. Through this aim, the experimental protocol involved 12 participants who performed three tasks on a treadmill consisting of one unperturbed and two perturbed walking tests. Inertial measurement units were used to gather lower limb kinematics. Parameters related to joint angles, as the range of motion (ROM) and its variability (CoV), as well as the inter-joint coordination in terms of continuous relative phase (CRP) were computed. The AP perturbation seemed to be more challenging causing differences with respect to normal walking in both the variability of the ROM and the CRP amplitude and variability. As ML, only the ankle showed different behavior in terms of joint angle and CRP variability. In both tasks, a shortening of the stance was found. The findings should be considered when implementing perturbed rehabilitative protocols for falling reduction.

Evaluation of Gait Characteristics in Subjects With Locomotive Syndrome Using the Wearable Gait Sensors

Background: Locomotive syndrome (LS) is a condition where a person requires nursing care services due to problems with locomotive abilities and musculoskeletal systems. Individuals with LS have a reduced walking speed compared to those without LS. However, differences in lower-limb kinematics and during walking between individuals with and without LS are not fully understood. The purpose of this study is to clarify the characteristics of gait kinematics using wearable sensors for individuals with LS.Methods: We assessed 125 people aged 65 years and older who utilized a public health promotion facility. The participants were grouped into Non-LS, LS-stage 1, LS-stage 2 (large number indicate worse locomotive ability) based on 25-question Geriatric Locomotive Function Scale (GLFS-25). Spatiotemporal parameters and lower-limb kinematics during 10-m walking test were analyzed by 7-inertia-sensors based motion analysis system. Peak joint angles during stance and swing phase as well as gait speed, cadence and step length were compared among all groups.Results: The number of each LS stage was 69, 33, 23 for Non-LS, LS-stage 1, LS-stage 2, respectively. LS-stage2 group showed significantly smaller peak hip extension angle, hip flexion angle and knee flexion angle than Non-LS group (hip extension: Non-LS: 9.5 ± 5.3°, LS-stage 2: 4.2 ± 8.2°, P = 0.002; hip flexion: No-LS: 34.2 ± 8.8°, LS-stage 2: 28.5 ± 9.5°, P = 0.026; knee flexion: Non-LS: 65.2 ± 18.7°, LS-stage 2: 50.6 ± 18.5°, P = 0.005). LS-stage 1 and LS-stage 2 groups showed significantly slower gait speed than Non-LS group (Non-LS 1.3 ± 0.2 m/s, LS-stage1 1.2 ± 0.2 m/s, LS-stage2 1.1 ± 0.2 m/s, P < 0.001).Conclusions: LS-stage2 group showed significantly different lower-limb kinematics compared with Non-LS group including smaller hip extension, hip flexion and knee flexion. The intervention based on these kinematic characteristics measured by wearable sensors would be useful to improve the locomotive ability for individuals classified LS-stage2.

Increasing Step Rate Affects Rearfoot Kinematics and Ground Reaction Forces during Running

Relatively high frontal and transverse plane motion in the lower limbs during running have been thought to play a role in the development of some running-related injuries (RRIs). Increasing step rate has been shown to significantly alter lower limb kinematics and kinetics during running. The purpose of this study was to evaluate the effects of increasing step rate on rearfoot kinematics, and to confirm how ground reaction forces (GRFs) are adjusted with increased step rate. Twenty runners ran on a force instrumented treadmill while marker position data were collected under three conditions. Participants ran at their preferred pace and step rate, then +5% and +10% of their preferred step rate while being cued by a metronome for three minutes each. Sagittal and frontal plane angles for the rearfoot segment, tibial rotation, and GRFs were calculated during the stance phase of running. Significant decreases were observed in sagittal and frontal plane rearfoot angles, tibial rotation, vertical GRF, and anteroposterior GRF with increased step rate compared with the preferred step rate. Increasing step rate significantly decreased peak sagittal and frontal plane rearfoot and tibial rotation angles. These findings may have implications for some RRIs and gait retraining.

Estimation of Lower Limb Kinematics during Squat Task in Different Loading Using sEMG Activity and Deep Recurrent Neural Networks

The aim of the present study was to predict the kinematics of the knee and the ankle joints during a squat training task of different intensities. Lower limb surface electromyographic (sEMG) signals and the 3-D kinematics of lower extremity joints were recorded from 19 body builders during squat training at four loading conditions. A long-short term memory (LSTM) was used to estimate the kinematics of the knee and the ankle joints. The accuracy, in terms root-mean-square error (RMSE) metric, of the LSTM network for the knee and ankle joints were 6.774 ± 1.197 and 6.961 ± 1.200, respectively. The LSTM network with inputs processed by cross-correlation (CC) method showed 3.8% and 4.7% better performance in the knee and ankle joints, respectively, compared to when the CC method was not used. Our results showed that in the prediction, regardless of the intensity of movement and inter-subject variability, an off-the-shelf LSTM decoder outperforms conventional fully connected neural networks.

Lower-limb kinematics and kinetics during continuously varying human locomotion

Human locomotion involves continuously variable activities including walking, running, and stair climbing over a range of speeds and inclinations as well as sit-stand, walk-run, and walk-stairs transitions. Understanding the kinematics and kinetics of the lower limbs during continuously varying locomotion is fundamental to developing robotic prostheses and exoskeletons that assist in community ambulation. However, available datasets on human locomotion neglect transitions between activities and/or continuous variations in speed and inclination during these activities. This data paper reports a new dataset that includes the lower-limb kinematics and kinetics of ten able-bodied participants walking at multiple inclines (±0°; 5° and 10°) and speeds (0.8 m/s; 1 m/s; 1.2 m/s), running at multiple speeds (1.8 m/s; 2 m/s; 2.2 m/s and 2.4 m/s), walking and running with constant acceleration (±0.2; 0.5), and stair ascent/descent with multiple stair inclines (20°; 25°; 30° and 35°). This dataset also includes sit-stand transitions, walk-run transitions, and walk-stairs transitions. Data were recorded by a Vicon motion capture system and, for applicable tasks, a Bertec instrumented treadmill.

A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons

The interaction between human and exoskeletons increasingly relies on the precise decoding of human motion. One main issue of the current motion decoding algorithms is that seldom algorithms provide both discrete motion patterns (e.g., gait phases) and continuous motion parameters (e.g., kinematics). In this paper, we propose a novel algorithm that uses the surface electromyography (sEMG) signals that are generated prior to their corresponding motions to perform both gait phase recognition and lower-limb kinematics prediction. Particularly, we first propose an end-to-end architecture that uses the gait phase and EMG signals as the priori of the kinematics predictor. In so doing, the prediction of kinematics can be enhanced by the ahead-of-motion property of sEMG and quasi-periodicity of gait phases. Second, we propose to select the optimal muscle set and reduce the number of sensors according to the muscle effects in a gait cycle. Finally, we experimentally investigate how the assistance of exoskeletons can affect the motion intent predictor, and we propose a novel paradigm to make the predictor adapt to the change of data distribution caused by the exoskeleton assistance. The experiments on 10 subjects demonstrate the effectiveness of our algorithm and reveal the interaction between assistance and the kinematics predictor. This study would aid the design of exoskeleton-oriented motion-decoding and human–machine interaction methods.

Prediction of gait trajectories based on the Long Short Term Memory neural networks

The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim of this work was to examine four Long Short Term Memory (LSTM) neural network architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in predicting the future trajectories of lower limb kinematics, i.e. Angular Velocity (AV) and Linear Acceleration (LA). Kinematics data of foot, shank and thigh (LA and AV) were collected from 13 male and 3 female participants (28 ± 4 years old, 1.72 ± 0.07 m in height, 66 ± 10 kg in mass) who walked for 10 minutes at preferred walking speed (4.34 ± 0.43 km.h-1) and at an imposed speed (5km.h-1, 15.4% ± 7.6% faster) on a 0% gradient treadmill. The sliding window technique was adopted for training and testing the LSTM models with total kinematics time-series data of 10,500 strides. Results based on leave-one-out cross validation, suggested that the LSTM autoencoders is the top predictor of the lower limb kinematics trajectories (i.e. up to 0.1s). The normalised mean squared error was evaluated on trajectory predictions at each time-step and it obtained 2.82–5.31% for the LSTM autoencoders. The ability to predict future lower limb motions may have a wide range of applications including the design and control of bionics allowing improved human-machine interface and mitigating the risk of falls and balance loss.