Accurate walking speed estimation in lower-limb prostheses is crucial for delivering biomechanically appropriate assistance across varying speeds. However, training robust models requires extensive domain-specific, user-dependent (DEP) data, which is impractical for every new prosthesis user. This study presents a transfer learning framework to simplify and enhance the training process. Convolutional neural networks were pre-trained on publicly available datasets from able-bodied (AB) individuals and transfemoral amputees using the Open Source Leg (OSL) knee-ankle prosthesis, then fine-tuned with data from a transfemoral amputee using the Power Knee (PK) prosthesis. The fine-tuned models, AB-PK and OSL-PK were trained with varying data amounts and evaluated across constant and variable walking speed trials, with performance compared to DEP models trained from scratch on PK data. Training and testing were conducted on a per-subject basis, with performance averaged across subjects (N=7). The lowest post-fine-tuning error was observed in AB-PK, with RMSE values of 0.041 m/s for constant speeds, 0.072 m/s for variable speeds, and 0.088 m/s for novel speeds not included in the original training data. Significant error reductions were observed in both fine-tuned models compared to DEP when fewer than 30 gait cycles per speed of training data were available. Notably, AB datasets appeared highly viable for this application and may even outperform OSL datasets in transfer learning for walking speed estimation, perhaps due to the much larger original training dataset. This approach highlights the potential of transfer learning across different subject populations and devices, offering insights into the data needed to achieve state-of-the-art speed estimation.

Human-robot teams in dynamic environments have the potential to leverage robot sensing and intelligence to augment human performance through motion suggestions. More specifically, we examine how humans can use external sensors (fixed or robotic) and a motion planning algorithm to help them navigate environments with dynamic obstacles. The novel human-centric velocity-obstacle (HCVO) algorithm suggests a feasible goal-oriented action while avoiding obstacles. Participants were placed in a custom virtual reality (VR) environment and tasked to follow a dynamic goal while avoiding collisions. We demonstrate, over N = 10 participants, that the HCVO algorithm’s guidance significantly improves safety compared to a base VO algorithm. We then examine the performance of N = 15 participants in three conditions: (1) no assistance/control, (2) a top-down drone-view of the entire environment, and 3) motion planner-informed suggestions. The core contributions of this research include (1) introducing and tuning of a human-centric velocity-obstacle (HCVO) algorithm, (2) demonstrating the benefits of the HCVO algorithm compared to a base VO algorithm, (3) demonstrating the benefits of the HCVO algorithm compared with a standard overhead drone view. Long term, the deployment of effective human-centric motion planners can make people safer from workplace to warzone. Code: https://github.com/ROAMR-GT/HCVO-Game . Video: https://www.youtube.com/watch?v=9ITD1GBBz24 .

Robotic exoskeletons can transform mobility for individuals with lower-limb disabilities. However, their widespread adoption is limited by controller degradation caused by varying gait dynamics across different users and environments. Here, we propose an online adaptation framework that leverages real-time data streams to continuously update the user state estimator model. This approach allows the exoskeleton to learn the user-specific gait patterns, effectively customizing the model for each new user. Additionally, we demonstrate a sensor signal transformation technique that enables model transfer across different exoskeleton hardware (from a research-grade exoskeleton to a commercial device). With less than one minute of adaptation, our framework improved gait phase estimation, which directly affects assistance timing, by 40.9% for able-bodied subjects and 65.9% for stroke survivors (p<0.05), and reduced torque profile error by 32.7% compared to the baseline model (p<0.05). Furthermore, in a pilot test, we applied our adaptation framework with human-in-the-loop optimization for control tuning. In a single stroke survivor, this approach led to a 21.8% increase in walking speed and a 6.5% reduction in metabolic cost compared to walking without exoskeleton. While preliminary, these results suggest the potential for personalized exoskeleton assistance in clinical populations.

Estimating human joint moments using wearable sensors has utility for personalized health monitoring and generalized exoskeleton control. Data-driven models have potential to map wearable sensor data to human joint moments, even with a reduced sensor suite and without subject-specific calibration. In this study, we quantified the RMSE and R2of a temporal convolutional network (TCN), trained to estimate human hip moments in the sagittal plane using exoskeleton sensor data (i.e., a hip encoder and thigh- and pelvis-mounted inertial measurement units). We conducted three analyses in which we iteratively retrained the network while: 1) varying the input sequence length of the model, 2) incorporating noncausal data into the input sequence, thus delaying the network estimates, and 3) time shifting the labels to train the model to anticipate (i.e., predict) human hip moments. We found that 930 ms of causal input data maintained model performance while minimizing input sequence length (validation RMSE and R2of 0.141±0.014 Nm/kg and 0.883±0.025, respectively). Further, delaying the model estimate by up to 200 ms significantly improved model performance compared to the best causal estimators (p2(p2>0.85) when predicting up to 200 ms ahead.

Lumbar spine injuries caused by repetitive lifting rank as the most prevalent workplace injury in the United States. While these injuries are caused by both symmetric and asymmetric lifting, asymmetric is often more damaging. Many back devices do not address asymmetry, so we present a new system called the Asymmetric Back Exosuit (ABX). The ABX addresses this important gap through unique design geometry and active cable-driven actuation. The suit allows the user to move in a wide range of lumbar trajectories while the “X” pattern cable routing allows variable assistance application for these trajectories. We also conducted a biomechanical analysis in OpenSim to map assistive cable force to effective lumbar torque assistance for a given trajectory, allowing for intuitive controller design in the lumbar joint space over the complex kinematic chain for varying lifting techniques. Human subject experiments illustrated that the ABX reduced lumbar erector spinae muscle activation during symmetric and asymmetric lifting by an average of 37.8% and 16.0%, respectively, compared to lifting without the exosuit. This result indicates the potential for our device to reduce lumbar injury risk.

Visual engagement is common in many situations where human operators must perform tasks in challenging environments. This visual engagement has the potential to impact the safety of these operators when dealing with dynamic threats. Perceptual cues have been shown to elicit physical evasion maneuvers, thereby improving safety. In this article, we investigate the effects of cues and visual engagement on rapid whole-body responses. The visual task, inspired by the Trail Making Test (TMT), served as a proxy for visual engagement in the real world. Our continuous TMT minigame and threat simulation are implemented in a virtual reality environment. Participants attempt to maximize their performance score by quickly solving TMTs and dodging dynamic threats from various in-plane directions. They are provided with no cues (control), visual cues, and vibrotactile cues indicating impending threat directions. Participant’s ability to dodge threats is quantified by failure rate and reaction time within field of view and for all approach directions. An index of difficulty highlighted perceptual cue response sensitivity to varying threat speeds and sizes. This article provides two core key contributions and other interesting findings: 1) the results illustrate that tactile cues enable statistically significantly better dodging rates than visual cues or with human vision alone (control condition); and 2) that visual engagement degrades human evasion performance in a statistically significant way. Finally, tactile cue responses appear to be less sensitive than visual cues to visually engaging tasks within the higher portion of difficulty index range that is investigated.

Objective: To estimate instantaneous oxygen uptake VO2with a small, low-cost wearable sensor during exercise and daily activities in order to enable monitoring of energy expenditure (EE) in uncontrolled settings. We aim to do so using a combination of seismocardiogram (SCG), electrocardiogram (ECG) and atmospheric pressure (AP) signals obtained from a minimally obtrusive wearable device. Methods: In this study, subjects performed a treadmill protocol in a controlled environment and an outside walking protocol in an uncontrolled environment. During testing, the COSMED K5 metabolic system collected gold standard breath-by-breath (B×B) data and a custombuilt wearable patch placed on the mid-sternum collected SCG, ECG and AP signals. We extracted features from these signals to estimate the B×B VO2data obtained from the COSMED system. Results: In estimating instantaneous VO2, we achieved our best results on the treadmill protocol using a combination of SCG (frequency) and AP features (RMSE of 3.68 ± 0.98 ml/kg/min and R2of 0.77). For the outside protocol, we achieved our best results using a combination of SCG (frequency), ECG and AP features (RMSE of 4.3 ± 1.47 ml/kg/min and R2of 0.64). In estimating VO2consumed over one minute intervals during the protocols, our median percentage error was 15.8% for the treadmill protocol and 20.5% for the outside protocol. Conclusion: SCG, ECG and AP signals from a small wearable patch can enable accurate estimation of instantaneous VO2in both controlled and uncontrolled settings. SCG signals capturing variation in cardio-mechanical processes, AP signals, and state of the art machine learning models contribute significantly to the accurate estimation of instantaneous VO2. Significance: Accurate estimation of VO2with a low cost, minimally obtrusive wearable patch can enable the monitoring of VO2and EE in everyday settings and make the many applications of these measurements more accessible to the general public.