Effects of stance control via hidden Markov model-based gait phase detection on healthy users of an active hip-knee exoskeleton.

Introduction: In the past years, robotic lower-limb exoskeletons have become a powerful tool to help clinicians improve the rehabilitation process of patients who have suffered from neurological disorders, such as stroke, by applying intensive and repetitive training. However, active subject participation is considered to be an important feature to promote neuroplasticity during gait training. To this end, the present study presents the performance assessment of the AGoRA exoskeleton, a stance-controlled wearable device designed to assist overground walking by unilaterally actuating the knee and hip joints. Methods: The exoskeleton's control approach relies on an admittance controller, that varies the system impedance according to the gait phase detected through an adaptive method based on a hidden Markov model. This strategy seeks to comply with the assistance-as-needed rationale, i.e., an assistive device should only intervene when the patient is in need by applying Human-Robot interaction (HRI). As a proof of concept of such a control strategy, a pilot study comparing three experimental conditions (i.e., unassisted, transparent mode, and stance control mode) was carried out to evaluate the exoskeleton's short-term effects on the overground gait pattern of healthy subjects. Gait spatiotemporal parameters and lower-limb kinematics were captured using a 3D-motion analysis system Vicon during the walking trials. Results and Discussion: By having found only significant differences between the actuated conditions and the unassisted condition in terms of gait velocity (ρ = 0.048) and knee flexion (ρ ≤ 0.001), the performance of the AGoRA exoskeleton seems to be comparable to those identified in previous studies found in the literature. This outcome also suggests that future efforts should focus on the improvement of the fastening system in pursuit of kinematic compatibility and enhanced compliance.

Development of a 3D Relative Motion Method for Human-Robot Interaction Assessment.

Exoskeletons have been assessed by qualitative and quantitative features known as performance indicators. Within these, the ergonomic indicators have been isolated, creating a lack of methodologies to analyze and assess physical interfaces. In this sense, this work presents a three-dimensional relative motion assessment method. This method quantifies the difference of orientation between the user's limb and the exoskeleton link, providing a deeper understanding of the Human-Robot interaction. To this end, the AGoRA exoskeleton was configured in a resistive mode and assessed using an optoelectronic system. The interaction quantified a difference of orientation considerably at a maximum value of 41.1 degrees along the sagittal plane. It extended the understanding of the Human-Robot Interaction throughout the three principal human planes. Furthermore, the proposed method establishes a performance indicator of the physical interfaces of an exoskeleton.

Does exhaustion modify acceleration running signature?

Previous studies have demonstrated the acceleration signal presents a typical running signature, which allows for the extraction of reliable information. However, few studies have focused on the exhaustion-induced variability of the acceleration signature during running. The present study included 10 participants who ran at a constant speed on a treadmill until exhaustion. The participants were equipped with three accelerometers, located at the lumbar spine, tibia, and foot. The results showed that all the participants kept a constant pace throughout the test (coefficient of variation <5%). Similarities between acceleration signatures were observed using the coefficient of multiple correlation. For the longitudinal axis of the lumbar spine, the longitudinal axis of the tibia, and the anteroposterior axis of the tibia, running signatures were not affected by exhaustion (coefficient of multiple correlation >0.8). For all the other axes, the signature was impacted within and between the states of exhaustion. Signatures were particularly different for the foot sensors, which makes it difficult to use to extract reliable information. The results showed that the coefficient of multiple correlation allowed the quantification of the variability of the running signature, and that each axis and measuring point varied in how they were influenced by exhaustion.

Evaluation of Physical Interaction during Walker-Assisted Gait with the AGoRA Walker: Strategies Based on Virtual Mechanical Stiffness.

Smart walkers are commonly used as potential gait assistance devices, to provide physical and cognitive assistance within rehabilitation and clinical scenarios. To understand such rehabilitation processes, several biomechanical studies have been conducted to assess human gait with passive and active walkers. Several sessions were conducted with 11 healthy volunteers to assess three interaction strategies based on passive, low and high mechanical stiffness values on the AGoRA Smart Walker. The trials were carried out in a motion analysis laboratory. Kinematic data were also collected from the smart walker sensory interface. The interaction force between users and the device was recorded. The force required under passive and low stiffness modes was 56.66% and 67.48% smaller than the high stiffness mode, respectively. An increase of 17.03% for the hip range of motion, as well as the highest trunk's inclination, were obtained under the resistive mode, suggesting a compensating motion to exert a higher impulse force on the device. Kinematic and physical interaction data suggested that the high stiffness mode significantly affected the users' gait pattern. Results suggested that users compensated their kinematics, tilting their trunk and lower limbs to exert higher impulse forces on the device.

A time to exhaustion model during prolonged running based on wearable accelerometers.

Defining relationships between running mechanisms and fatigue can be a major asset for optimising training. This article proposes a biomechanical model of time to exhaustion according to indicators derived from accelerometry data collected from the body. Ten volunteers were recruited for this study. The participants were equipped with 3 accelerometers: on the right foot, at the tibia and at the L4-L5 lumbar spine. A running test was performed on a treadmill at 13.5 km/h until exhaustion. Thirty-one variables were deployed during the test. Multiple linear regressions were calculated to explain the time to exhaustion from the indicators calculated on the lumbar, tibia and foot individually and simultaneously. Time to exhaustion was predicted for simultaneous measurement points with r 2 = 0.792 and 21 indicators; for the lumbar with r 2 = 0.568 and 11 indicators; for the tibia with r 2 = 558 and 11 indicators; and for the foot with r 2 = 0.626 and 12 indicators. This study allows the accurate modelling of the time to exhaustion during a running-based test using indicators from accelerometer measurements. The individual models highlight that the location of the measurement point is important and that each location provides different information. Future studies should focus on homogeneous populations to improve predictions and errors.

Effect of running speed on temporal and frequency indicators from wearable MEMS accelerometers.

Amplified by the development of new technologies, the interest in personal performance has been growing over the last years. Acceleration has proved to be an easy variable to collect, and was addressed in several works. However, few of them evaluate the effect of running speed on relevant indicators. The influence of the sensors location on the measurement is rarely studied as well. This study is dedicated to investigating the effect of running speed on acceleration measured at three different positions on 18 volunteers. All participants were equipped with three inertial measurement units: on the dorsal surface of the right foot (Fo), at the centre of gravity of the tibia (Ti), at the L4-L5 lumbar (Lu). The test was performed on a treadmill at nine randomised speeds between 8 and 18 km/h. Ten accelerometric variables were calculated. Linear regressions were used to calculate speed from the indicators calculated on (Lu), (Ti), (Fo). Indicators associated to signal energy were highly correlated with speed (r2>0.90). Median frequency appears to be affected by the frequency resolution. Finally, the measurement points closest to the impact zone result in the most correlated indicators.

Validation of a High Sampling Rate Inertial Measurement Unit for Acceleration During Running.

The musculo-skeletal response of athletes to various activities during training exercises has become a critical issue in order to optimize their performance and minimize injuries. However, dynamic and kinematic measures of an athlete's activity are generally limited by constraints in data collection and technology. Thus, the choice of reliable and accurate sensors is crucial for gathering data in indoor and outdoor conditions. The aim of this study is to validate the use of the accelerometer of a high sampling rate ( 1344 Hz ) Inertial Measurement Unit (IMU) in the frame of running activities. To this end, two validation protocols are imposed: a classical one on a shaker, followed by another one during running, the IMU being attached to a test subject. For each protocol, the response of the IMU Accelerometer (IMUA) is compared to a calibrated industrial accelerometer, considered as the gold standard for dynamic and kinematic data collection. The repeatability, impact of signal frequency and amplitude (on shaker) as well as the influence of speed (while running) are investigated. Results reveal that the IMUA exhibits good repeatability. Coefficient of Variation CV is 1 % 8.58 ± 0.06 m / s 2 on the shaker and 3 % 26.65 ± 0.69 m / s 2 while running. However, the shaker test shows that the IMUA is affected by the signal frequency (error exceeds 10 % beyond 80 Hz ), an observation confirmed by the running test. Nevertheless, the IMUA provides a reliable measure in the range 0-100 Hz, i.e., the most relevant part in the energy spectrum over the range 0-150 Hz during running. In our view, these findings emphasize the validity of IMUs for the measurement of acceleration during running.