Impact of Haptic Cues and an Active Ankle Exoskeleton on Gait Characteristics.

OBJECTIVE: This study examined the interaction of gait-synchronized vibrotactile cues with an active ankle exoskeleton that provides plantarflexion assistance. BACKGROUND: An exoskeleton that augments gait may support collaboration through feedback to the user about the state of the exoskeleton or characteristics of the task. METHODS: Participants (N = 16) were provided combinations of torque assistance and vibrotactile cues at pre-specified time points in late swing and early stance while walking on a self-paced treadmill. Participants were either given explicit instructions (N = 8) or were allowed to freely interpret (N=8) how to coordinate with cues. RESULTS: For the free interpretation group, the data support an 8% increase in stride length and 14% increase in speed with exoskeleton torque across cue timing, as well as a 5% increase in stride length and 7% increase in speed with only vibrotactile cues. When given explicit instructions, participants modulated speed according to cue timing-increasing speed by 17% at cues in late swing and decreasing speed 11% at cues in early stance compared to no cue when exoskeleton torque was off. When torque was on, participants with explicit instructions had reduced changes in speed. CONCLUSION: These findings support that the presence of torque mitigates how cues were used and highlights the importance of explicit instructions for haptic cuing. Interpreting cues while walking with an exoskeleton may increase cognitive load, influencing overall human-exoskeleton performance for novice users. APPLICATION: Interactions between haptic feedback and exoskeleton use during gait can inform future feedback designs to support coordination between users and exoskeletons.

Assessment of a powered ankle exoskeleton on human stability and balance.

Wearable robotic systems, such as exoskeletons, are designed to assist human motion; however, they are typically only studied during level walking. Before exoskeletons are broadly integrated into unstructured environments, it will be important to evaluate exoskeletons in a broader set of relevant tasks. A balance beam traverse was used to represent a constrained foot placement task for examining balance and stability. Participants (n = 17) completed the task in their own shoes (Pre-Exoskeleton and Post-Exoskeleton trials), and when wearing a lower-limb exoskeleton (Dephy ExoBoot) in both powered and unpowered states. Data were collected via inertial measurement units (on the torso and feet) and analyzed on a pooled level (with data from all participants) and on an individual level (participant-specific confidence intervals). When examining pooled data, it was observed that the exoskeleton had mixed effects on stride stability metrics. When compared to the Post-Exoskeleton shoe control, it was observed that stride duration was increased when wearing the exoskeleton (both powered and unpowered states), while normalized stride length and stride speed were not affected. Despite the changes in stride stability, overall balance (as measured by torso sway) remained unaffected by exoskeleton state. On an individual level, it was observed that not all participants followed these general trends, and within each metric, some increased, some decreased, and some had no change in the Powered Exoskeleton condition when compared to the Post-Exoskeleton Shoe condition: normalized stride length (0% increased, 12% decreased, 88% no change), stride duration (35% increased, 0% decreased, 65% no change), and torso sway (0% increased, 12% decreased, 88% no change). Our findings suggest that the lower-limb exoskeleton evaluated can be used during tasks that require balancing, and we recommend that balancing tasks be included in standards for exoskeleton evaluation.

Utility of Inter-subject Transfer Learning for Wearable-Sensor-Based Joint Torque Prediction Models.

Generalizability between individuals and groups is often a significant hurdle in model development for human subjects research. In the domain of wearable-sensor-controlled exoskeleton devices, the ability to generalize models across subjects or fine-tune more general models to individual subjects is key to enabling widespread adoption of these technologies. Transfer learning techniques applied to machine learning models afford the ability to apply and investigate the viability and utility such knowledge-transfer scenarios. This paper investigates the utility of single- and multi-subject based parameter transfer on LSTM models trained for "sensor-to-joint torque" prediction tasks, with regards to task performance and computational resources required for network training. We find that parameter transfer between both single- and multi-subject models provide useful knowledge transfer, with varying results across specific "source" and "target" subject pairings. This could be leveraged to lower model training time or computational cost in compute-constrained environments or, with further study to understand causal factors of the observed variance in performance across source and target pairings, to minimize data collection and model retraining requirements to select and personalize a generic model for personalized wearable-sensor-based joint torque prediction technologies.

Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running.

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

Robotic Spine Exoskeleton (RoSE): Characterizing the 3-D Stiffness of the Human Torso in the Treatment of Spine Deformity.

Spine deformity is typically treated with a brace that fits around the torso and hips to correct the abnormal curve of the spine. While bracing has been shown to curtail progression of abnormal spine curves, current braces impose several limitations due to their rigid, static, and sensor-less designs: (1) forces and moments exerted by the brace cannot be measured or modulated and (2) the 3-D stiffness of the human torso has not been characterized-these may be important factors to be considered in bracing treatment. We address these limitations using a robotic spine exoskeleton (RoSE), capable of controlling the position/orientation of specific cross sections of the human torso while simultaneously measuring the forces/moments exerted on the body. Eight healthy subjects and two subjects with spine deformity participated in a study to characterize the 3-D stiffness of their torso. The results show that the 3-D stiffness of human torso can be characterized using RoSE and indicated that the spine deformities induce torso stiffness characteristics significantly different from the healthy subjects. These characteristics are curve-specific and present a pronounced asymmetry. These results open up the possibility for the design of spine braces incorporating patient specific torso stiffness characteristics and potential for new interventions using the dynamic modulation of 3-D forces for spine deformity treatment.

Variable Damping Force Tunnel for Gait Training Using ALEX III.

Haptic feedback affects not only the quality of training but can also influence the physical design of robotic gait trainers by determining how much force needs to be applied to the user and the nature of the force. This paper presents the design of a variable damping force tunnel and explores the effect of the shape and strength of the damping field using ALEX III, a treadmill-based exoskeleton developed at Columbia University. The study consists of 32 healthy subjects who were trained for 40 minutes in the device. The subjects were trained to follow a footpath with a 50% increase in step height, so the foot would have 1.5 times the ground clearance. Subjects were assigned to one of four groups: linear high, linear low, parabolic high, and parabolic low. Linear or parabolic denotes the shape of the damping field, and high or low denotes the rate of change (strength) of the field based on error. It is shown that the new controller is capable of inducing gait adaptations in healthy individuals while walking in the device. All groups showed adaptations in step height, while only the high strength groups showed changes in normalized error area, a measure of how closely the desired path was followed.

Design and preliminary evaluation of a multi-robotic system with pelvic and hip assistance for pediatric gait rehabilitation.

This paper presents a modular, computationally-distributed "multi-robot" cyberphysical system designed to assist children with developmental delays in learning to walk. The system consists of two modules, each assisting a different aspect of gait: a tethered cable pelvic module with up to 6 degrees of freedom (DOF), which can modulate the motion of the pelvis in three dimensions, and a two DOF wearable hip module assisting lower limb motion, specifically hip flexion. Both modules are designed to be lightweight and minimally restrictive to the user, and the modules can operate independently or in cooperation with each other, allowing flexible system configuration to provide highly customized and adaptable assistance. Motion tracking performance of approximately 2 mm root mean square (RMS) error for the pelvic module and less than 0.1 mm RMS error for the hip module was achieved. We demonstrate coordinated operation of the two modules on a mannequin test platform with articulated and instrumented lower limbs.

Walking With aBackpack Using Load Distribution and Dynamic Load Compensation Reduces Metabolic Cost and Adaptations to Loads.

In this study, we showed a way of reducing the metabolic cost of walking with a backpack using load distribution and dynamic load compensation, provided by a wearable upper body device. This device distributes the backpackload between the shouldersand the pelvis, senses the vertical motion of the pelvis, and provides gait synchronized compensatory forces to reduce the dynamic loads from a backpack. It was hypothesized that by reducing dynamic loads from a backpack during load carriage, the user's gait and postural adaptation, muscular effort and metabolic cost would be reduced. This hypothesis was supported by biomechanical and physiological measurements on a group of young healthy subjects, as they walked on a treadmill under four different conditions: unloaded; with a backpack, loaded with 25% of their body weight, supported on the shoulders; with the same load distributed between the shoulders and the pelvis; and with dynamic load compensation in addition to load distribution. The results showed reductions in gait and postural adaptations, muscle activity, vertical and braking ground reaction forces, and metabolic cost while carrying the same backpack load with the device. We conclude that the device can potentially reduce the risk of musculoskeletal injuries and muscle fatigue associated with carrying heavy backpack loads while reducing the metabolic cost of loaded walking.

Assist-as-Needed Robot-Aided Gait Training Improves Walking Function in Individuals Following Stroke.

A novel robot-aided assist-as-needed gait training paradigm has been developed recently. This paradigm encourages subjects' active participation during training. Previous pilot studies demonstrated that assist-as-needed robot-aided gait training (RAGT) improves treadmill walking performance post-stroke. However, it is not known if there is an over-ground transfer of the training effects from RAGT on treadmill or long-term retention of the effects. The purpose of the current study was to examine the effects of assist-as-needed RAGT on over-ground walking pattern post-stroke. Nine stroke subjects received RAGT with visual feedback of each subject's instantaneous ankle malleolus position relative to a target template for 15 40-minute sessions. Clinical evaluations and gait analyses were performed before, immediately after, and 6 months post-training. Stroke subjects demonstrated significant improvements and some long-term retention of the improvements in their self-selected over-ground walking speed, Dynamic Gait Index, Timed Up and Go, peak knee flexion angle during swing phase and total hip joint excursion over the whole gait cycle for their affected leg . These preliminary results demonstrate that subjects improved their over-ground walking pattern and some clinical gait measures post-training suggesting that assist-as-needed RAGT including visual feedback may be an effective approach to improve over-ground walking pattern post-stroke.

Directed neural connectivity changes in robot-assisted gait training: a partial Granger causality analysis.

Now-a-days robotic exoskeletons are often used to help in gait training of stroke patients. However, such robotic systems have so far yielded only mixed results in benefiting the clinical population. Therefore, there is a need to investigate how gait learning and de-learning get characterised in brain signals and thus determine neural substrate to focus attention on, possibly, through an appropriate brain-computer interface (BCI). To this end, this paper reports the analysis of EEG data acquired from six healthy individuals undergoing robot-assisted gait training of a new gait pattern. Time-domain partial Granger causality (PGC) method was applied to estimate directed neural connectivity among relevant brain regions. To validate the results, a power spectral density (PSD) analysis was also performed. Results showed a strong causal interaction between lateral motor cortical areas. A frontoparietal connection was found in all robot-assisted training sessions. Following training, a causal "top-down" cognitive control was evidenced, which may indicate plasticity in the connectivity in the respective brain regions.

Improving transparency of powered exoskeletons using force/torque sensors on the supporting cuffs.

Most of the control strategies embedded in recent robotic exoskeletons for rehabilitation and assistance are specific implementations of the well-known "assistance as needed" paradigm. A key point in the design of these systems is the requirement for the robot to exert negligible interaction forces to the wearer if he/she is performing well. Optimizing transparency of a device is a challenging task: various strategies have been proposed to achieve this goal, involving both the mechanical structure of the robot and the control algorithms. In this work, we propose a simple yet effective approach that requires minimal redesign efforts in the robotic structure and in the controller to be implemented on existing devices. We experimentally validate the method by comparing kinematic, kinetic and electromyo-graphic data collected from 3 healthy subjects as they walked in three different conditions: free treadmill walking, walking in a robotic trainer with a traditional zero-impedance configuration and walking in the same robot with the new zero-impedance configuration. Results show that the novel configuration was capable of effectively reducing the interaction forces and, as a consequence, it affected subjects' natural gait less than the traditional one did.

Effects of complementary auditory feedback in robot-assisted lower extremity motor adaptation.

This study investigates how complementary auditory feedback may affect short-term gait modifications induced by four training sessions with a robotic exoskeleton. Healthy subjects walked on a treadmill and were instructed to match a modified gait pattern derived from their natural one, while receiving assistance by the robot (kinetic guidance). The main question we wanted to answer is whether the most commonly used combination of feedback (i.e., haptic and visual) could be either enhanced by adding auditory feedback or successfully substituted with a combination of kinetic guidance and auditory feedback. Participants were randomly assigned to one of four groups, all of which received kinetic guidance. The control group received additional visual feedback, while the three experimental groups were each provided with a different modality of auditory feedback. The third experimental group also received the same visual feedback as the control group. Differences among the training modalities in gait kinematics, timing and symmetry were assessed in three post-training sessions.

Design of a minimally constraining, passively supported gait training exoskeleton: ALEX II.

This paper discusses the design of a new, minimally constraining, passively supported gait training exoskeleton known as ALEX II. This device builds on the success and extends the features of the ALEX I device developed at the University of Delaware. Both ALEX (Active Leg EXoskeleton) devices have been designed to supply a controllable torque to a subject's hip and knee joint. The current control strategy makes use of an assist-as-needed algorithm. Following a brief review of previous work motivating this redesign, we discuss the key mechanical features of the new ALEX device. A short investigation was conducted to evaluate the effectiveness of the control strategy and impact of the exoskeleton on the gait of six healthy subjects. This paper concludes with a comparison between the subjects' gait both in and out of the exoskeleton.