Biology subjects

Slade, P.

Publications and source records attributed to Slade, P..

4 recordsLinked to original sources

bioRxiv2026

Experiment-free learning of exoskeleton assistance remains an unsolved problem

In "Experiment-free exoskeleton assistance via learning in simulation", Luo et al. [1] present an ambitious framework for developing exoskeleton controllers through reinforcement learning exclusively in computer simulation. The authors report that a control policy trained on a small dataset from one subject was directly transferred to physical hardware, reducing human metabolic cost during walking, running, and stair climbing by more than any prior device. If confirmed, this would represent a major breakthrough for the field of wearable robotics and their clinical applications. However, a close examination of the published materials casts doubt on these claims. The reported experimental results violate physiological limits on the relationship between mechanical power and muscle energy use during gait2,3,4. The algorithmic claims are surprising and cannot be verified; in contrast with established replicability standards in machine learning5,6, executable code has not been made available. We conclude that the goals of this study have not yet been verifiably achieved and make recommendations for avoiding publication errors of this type in the future.

physiology↗

bioRxiv2024

Simulating human-in-the-loop optimization of exoskeleton assistance to compare optimization algorithm performance

Assistive robotic devices like exoskeletons offer the promise of improving mobility for millions of people. However, developing devices that improve an objective mobility metric is challenging. Human-in-the-loop optimization is a systematic approach for personalizing robotic assistance to maximize a mobility metric that has improved device performance for different metrics and applications. Successfully performing human-in-the-loop optimization requires the experimenter to make many decisions, like selecting the appropriate optimization algorithm, hyperparameters, and convergence criteria. Typically, selecting these experimental settings involves pilot experimentation. We propose an approach that uses a probabilistic surrogate model, mapping assistance parameters to corresponding experimental evaluations of the objective mobility metric, to simulate human-in-the-loop optimization and inform these decisions. In this paper, we form a surrogate model of the metabolic landscape of walking with exoskeleton assistance using an existing experimental dataset. We simulate human-in-the-loop optimization by using a synthetic metabolic landscape model to evaluate the metabolic cost of walking with different assistance parameters, instead of performing an experimental measurement. We perform three simulated scenarios optimizing assistance for an expert subject, a novice subject adapting to the device, and an expert subject with up to 20 assistance parameters. The code and analyses from this work are open-source to promote use by other researchers. Simulation enables direct comparison of optimization settings to inform experimental human-in-the-loop optimization and potentially reduce the resources and time required to develop effective assistive devices.

bioengineering↗

bioRxiv2021

An open-source and wearable system for measuring 3D human motion in real-time

Analyzing human motion is essential for diagnosing movement disorders and guiding rehabilitation interventions for conditions such as osteoarthritis, stroke, and Parkinsons disease. Optical motion capture systems are the current standard for estimating kinematics but require expensive equipment located in a predefined space. While wearable sensor systems can estimate kinematics in any environment, existing systems are generally less accurate than optical motion capture. Further, many wearable sensor systems require a computer in close proximity and rely on proprietary software, making it difficult for researchers to reproduce experimental findings. Here, we present OpenSenseRT, an open-source and wearable system that estimates upper and lower extremity kinematics in real time by using inertial measurement units and a portable microcontroller. We compared the OpenSenseRT system to optical motion capture and found an average RMSE of 4.4 degrees across 5 lower-limb joint angles during three minutes of walking (n = 5) and an average RMSE of 5.6 degrees across 8 upper extremity joint angles during a Fugl-Meyer task (n = 5). The open-source software and hardware are scalable, tracking between 1 and 14 body segments, with one sensor per segment. Kinematics are estimated in real-time using a musculoskeletal model and inverse kinematics solver. The computation frequency, depends on the number of tracked segments, but is sufficient for real-time measurement for many tasks of interest; for example, the system can track up to 7 segments at 30 Hz in real-time. The system uses off-the-shelf parts costing approximately $100 USD plus $20 for each tracked segment. The OpenSenseRT system is accurate, low-cost, and simple to replicate, enabling movement analysis in labs, clinics, homes, and free-living settings.

bioengineering↗

bioRxiv2018

Rapid energy expenditure estimation for assisted and inclined loaded walking

BackgroundEstimating energy expenditure with indirect calorimetry requires expensive equipment and provides slow and noisy measurements. Rapid estimates using wearable sensors would enable techniques like optimizing assistive devices outside a lab. Existing methods correlate data from wearable sensors to measured energy expenditure without evaluating the accuracy of the estimated energy expenditure for activity conditions or subjects not included in the correlation process. Our goal is to assess data-driven models that are capable of rapidly estimating energy expenditure for new conditions and subjects.\n\nMethodsWe developed models that estimated energy expenditure from two datasets during walking conditions with (1) ankle exoskeleton assistance and (2) various loads and inclines. The estimation was portable and rapid, using input features that are possible to measure with wearable sensors and restricting the input data length to a single gait cycle or four second interval. The performance of the models was evaluated for three use cases. The first case estimated energy expenditure during walking conditions for subjects with some subject specific training data available. The second case estimated all conditions in the dataset for a new subject not included in the training data. The third case estimated new conditions for a new subject. The models also ordered the magnitude of energy expenditure across all conditions for a new subject.\n\nResultsThe average errors in energy expenditure estimation during assisted walking conditions were 4.4%, 8.0%, and 8.1% for the three use cases, respectively. The average errors in energy expenditure estimation during inclined and loaded walking conditions were 6.1%, 9.7%, and 11.7% for the three use cases. The models ordered the magnitude of energy expenditure with a maximum and average percentage of correctly ordered conditions of 56% and 43% for assisted walking and 85% and 55% for incline and loaded walking.\n\nConclusionsOur data-driven models determined the accuracy of energy expenditure estimation for three use cases. For experiments where the accuracy of a data-driven model is sufficient, standard indirect calorimetry can be replaced. The energy expenditure ordering could aid in selecting optimal assistance conditions. The models, code, and datasets are provided for reproduction and extension of our results.

bioengineering↗