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Enhancing Robot Transparency in Human-Robot Prosthesis Interaction to Mitigate Terrain Misrecognition Error
IEEE Transactions on Medical Robotics and Bionics
Year: 2025 | Early Access Article |
Clear and effective communication between humans and robots is crucial when they work closely together. As wearable robots become more intelligent and automated, anticipatory control is limited for amputees because they lack prior knowledge of the timing and nature of changes in the robot’s motion, making human-machine collaboration more challenging. This study addresses the need for improved wear...Show More
Soft Variable Resistance Material for Pressure Sensing Platform
Sooyoung Kim;Tushar Sakorikar;He Huang;Michael D. Dickey;Ming Liu
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2024 | Conference Paper |
Herein, we present the mechanical response of two porous and conductive foams made of polydimethylsiloxane (PDMS) and polyurethane (PU). A coating of carbon nanotubes (CNTs) renders the foams with positive piezo-conductivity, which is useful for sensing mechanical loads. Such materials are useful as sensors to improve the fitting process of a prosthetic socket for lower limb amputees, PDMS and PU ...Show More
Exploring the Impact of Continuous Mediolateral Torque Application by Hip Exoskeleton on the Gait Balance of Non-Disabled Individuals
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2024 | Conference Paper |
This preliminary study investigates the potential of mediolaterally active robotic hip exoskeleton to influence mediolateral balance of non-disabled individuals. As no prior study has specifically demonstrated their impact on frontal plane gait balance, the primary objective was to examine the effects of a hip exoskeleton applying continuous mediolateral torque on predominant balance metrics commo...Show More
Assisting Upper Limb Prosthesis with a Computer Vision System for Material Detection
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2024 | Conference Paper |
The concept of utilizing computer vision to aid in the control of prosthetic limbs has been explored in recent studies. However, many computer vision systems are challenged by noisy and cluttered backgrounds in realistic scenarios. In this study, we investigated the feasibility of using a state-of-the-art object detection model (YOLOv8) to identify objects grasped by a myoelectric prosthetic hand....Show More
Continuous Gait Phase Estimation from Translational Kinematics: Towards Implementation in Powered Ankle Prostheses
Austin L. Mituniewicz;Woolim Hong;Michael D. Lewek;He Helen Huang
2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)
Year: 2024 | Conference Paper |
For people with transtibial amputations (PwTTA), discontinuities in powered prosthesis control schemes can lead to a disastrous trip or fall. Thus, continuous gait phase estimators have gained attention to facilitate smoother and more natural feeling device control. However, existing continuous estimators often struggle to generalize between steps, speeds, tasks, people, and impairments. This work...Show More
An Efficient Framework for Personalizing EMG-Driven Musculoskeletal Models Based on Reinforcement Learning
This study aimed to develop a novel framework to quickly personalize electromyography (EMG)-driven musculoskeletal models (MMs) as efferent neural interfaces for upper limb prostheses. Our framework adopts a generic upper-limb MM as a baseline and uses an artificial neural network-based policy to fine-tune the model parameters for MM personalization. The policy was trained by reinforcement learnin...Show More
Toward Task-Independent Optimal Adaptive Control of a Hip Exoskeleton for Locomotion Assistance in Neurorehabilitation
Qiang Zhang;Jennie Si;Xikai Tu;Minhan Li;Michael D. Lewek;He Huang
Personalized robotic exoskeleton control is essential in assisting individuals with motor deficits. However, current research still lacks a solution from the end of a practical need of the problem to the end of its successful demonstration in physical environments, namely an end-to-end solution, that enables stable and continuous walking across different tasks. This study addresses this challenge ...Show More
Human-Centered Evaluation of EMG-Based Upper-Limb Prosthetic Control Modes
Yunmei Liu;Joseph Berman;Albert Dodson;Junho Park;Maryam Zahabi;He Huang;Jaime Ruiz;David B. Kaber
The aim of this study was to experimentally test the effects of different electromyographic-based prosthetic control modes on user task performance, cognitive workload, and perceived usability to inform further human-centered design and application of these prosthetic control interfaces. We recruited 30 able-bodied participants for a between-subjects comparison of three control modes: direct contr...Show More
Abduction/Adduction Assistance From Powered Hip Exoskeleton Enables Modulation of User Step Width During Walking
Abbas Alili;Aaron Fleming;Varun Nalam;Ming Liu;Jesse Dean;He Huang
Using wearable robotics to modulate step width in normal walking for enhanced mediolateral balance has not been demonstrated in the field. We designed a bilateral hip exoskeleton with admittance control to power hip abduction and adduction to modulate step width. Objective: As the first step to show its potential, the objective of this study was to investigate how human's step width reacted to hip...Show More
Reinforcement Learning Control With Knowledge Shaping
We aim at creating a transfer reinforcement learning framework that allows the design of learning controllers to leverage prior knowledge extracted from previously learned tasks and previous data to improve the learning performance of new tasks. Toward this goal, we formalize knowledge transfer by expressing knowledge in the value function in our problem construct, which is referred to as reinforc...Show More
Cognitive Workload and Usability of Virtual Reality Simulation for Prosthesis Training
Junho Park;Austin Music;Daniel Delgado;Joseph Berman;Albert Dodson;Yunmei Liu;Jaime Ruiz;He Huang;David Kaber;Maryam Zahabi
2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Amputees use prosthetic devices to perform activities of daily living. However, some users reject their devices due to the lack of usability or high cognitive workload. Although virtual reality has been studied in this domain for training purposes, there has not been any investigation on usability and cognitive workload of using virtual reality simulations for training of prosthetic devices. The o...Show More
A Robotic Assistance Personalization Control Approach of Hip Exoskeletons for Gait Symmetry Improvement
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2023 | Conference Paper |
Healthy human locomotion functions with good gait symmetry depend on rhythmic coordination of the left and right legs, which can be deteriorated by neurological disorders like stroke and spinal cord injury. Powered exoskeletons are promising devices to improve impaired people's locomotion functions, like gait symmetry. However, given higher uncertainties and the time-varying nature of human-robot ...Show More
Ankle Torque Estimation With Motor Unit Discharges in Residual Muscles Following Lower-Limb Amputation
Noah Rubin;Robert Hinson;Katherine Saul;Xiaogang Hu;He Huang
There has been increased interest in using residual muscle activity for neural control of powered lower-limb prostheses. However, only surface electromyography (EMG)-based decoders have been investigated. This study aims to investigate the potential of using motor unit (MU)-based decoding methods as an alternative to EMG-based intent recognition for ankle torque estimation. Eight people without am...Show More
Towards Personalized Control for Powered Knee Prostheses: Continuous Impedance Functions and PCA-Based Tuning Method
2023 International Conference on Rehabilitation Robotics (ICORR)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Optimizing control parameters is crucial for personalizing prosthetic devices. The current method of finite state machine impedance control (FSM-IC) allows interaction with the user but requires time-consuming manual tuning. To improve efficiency, we propose a novel approach for tuning knee prostheses using continuous impedance functions (CIFs) and Principal Component Analysis (PCA). The CIFs, whi...Show More
Investigating the Association of Quantitative Gait Stability Metrics With User Perception of Gait Interruption Due to Control Faults During Human-Prosthesis Interaction
This study aims to compare the association of different gait stability metrics with the prosthesis users’ perception of their own gait stability. Lack of perceived confidence on the device functionality can influence the gait pattern, level of daily activities, and overall quality of life for individuals with lower limb motor deficits. However, the perception of gait stability is subjective and di...Show More
1-D Manual Tracing Based on a High Density Haptic Stimulation Grid - a Pilot Effort
2023 IEEE World Haptics Conference (WHC)
Year: 2023 | Conference Paper |
Lower limb amputees lack the neurological path-ways needed for perception of how their prosthetic limbs are interacting with the environment, leading to a lack of confidence in their devices and reduced balancing capabilities. Sensory substitution methods, such as vibrotactile and electrotactile feedback applied to unaffected body segments offer a potential way to restore some of the lost informat...Show More
Offline Evaluation Matters: Investigation of the Influence of Offline Performance of EMG-Based Neural-Machine Interfaces on User Adaptation, Cognitive Load, and Physical Efforts in a Real-Time Application
Robert M. Hinson;Joseph Berman;I-Chieh Lee;William G. Filer;He Huang
There has been controversy about the value of offline evaluation of EMG-based neural-machine interfaces (NMIs) for their real-time application. Often, conclusions have been drawn after studying the correlation of the offline EMG decoding accuracy/error with the NMI user’s real-time task performance without further considering other important human performance metrics such as adaptation rate, cogni...Show More
A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis
Abbas Alili;Varun Nalam;Minhan Li;Ming Liu;Jing Feng;Jennie Si;He Huang
The tuning of robotic prosthesis control is essential to provide personalized assistance to individual prosthesis users. Emerging automatic tuning algorithms have shown promise to ease the device personalization procedure. However, very few automatic tuning algorithms consider the user preference as the tuning goal, which may limit the adoptability of the robotic prosthesis. In this study, we prop...Show More
Offline Evaluation Matters: Investigation of the Influence of Offline Performance on Real-Time Operation of Electromyography-Based Neural-Machine Interfaces
Robert M. Hinson;Joseph Berman;William Filer;Derek Kamper;Xiaogang Hu;He Huang
There has been a debate on the most appropriate way to evaluate electromyography (EMG)-based neural-machine interfaces (NMIs). Accordingly, this study examined whether a relationship between offline kinematic predictive accuracy (R2) and user real-time task performance while using the interface could be identified. A virtual posture-matching task was developed to evaluate motion capture-based cont...Show More
Hierarchical Optimization for Control of Robotic Knee Prostheses Toward Improved Symmetry of Propulsive Impulse
Minhan Li;Wentao Liu;Jennie Si;Jonathan W. Stallrich;He Huang
Automatically personalizing complex control of robotic prostheses to improve gait performance, such as gait symmetry, is challenging. Recently, human-in-the-loop (HIL) optimization and reinforcement learning (RL) have shown promise in achieving optimized control of wearable robots for each individual user. However, HIL optimization methods lack scalability for high-dimensional space, while RL has ...Show More
Harnessing Machine Learning and Physiological Knowledge for a Novel EMG-Based Neural-Machine Interface
Objective: In this study, we aimed to develop a novel electromyography (EMG)-based neural machine interface (NMI), called the Neural Network-Musculoskeletal hybrid Model (N2M2), to decode continuous joint angles. Our approach combines the concepts of machine learning and musculoskeletal modeling. Methods: We compared our novel design with a musculoskeletal model (MM) and 2 continuous EMG decoders ...Show More
Design of EMG-driven Musculoskeletal Model for Volitional Control of a Robotic Ankle Prosthesis
Chinmay Shah;Aaron Fleming;Varun Nalam;Ming Liu;He Helen Huang
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2022 | Conference Paper |
Cited by: Papers (7)
Existing robotic lower-limb prostheses use autonomous control to address cyclic, locomotive tasks, but are inadequate in adapting to variations in non-cyclic and unpredictable tasks. This study aims to address this challenge by designing a novel electromyography (EMG)-driven musculoskeletal model for volitional control of a robotic ankle-foot prosthesis. The proposed controller ensures continuous ...Show More
Cognitive Workload Classification of Upper-limb Prosthetic Devices
Junho Park;Joseph Berman;Albert Dodson;Yunmei Liu;Armstrong Matthew;He Huang;David Kaber;Jaime Ruiz;Maryam Zahabi
2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Limb amputation can cause severe functional disability in performing activities of daily living (ADLs). Using prosthetic devices as aids for such activities requires substantial cognitive resources. Machine Learning (ML) algorithms can be used to predict cognitive workload (CW) of prosthetic device prototypes early in the design process and serve as a tool for improving device usability. The objec...Show More
Toward Safe Wearer-Prosthesis Interaction: Evaluation of Gait Stability and Human Compensation Strategy Under Faults in Robotic Transfemoral Prostheses
I-Chieh Lee;Ming Liu;Michael D. Lewek;Xiaogang Hu;William G. Filer;He Huang
Although advanced wearable robots can assist human wearers, their internal faults (i.e., sensors or control errors) also pose a challenge. To ensure safe wearer-robot interactions, how internal errors by the prosthesis limb affect the stability of the user-prosthesis system, and how users react and compensate for the instability elicited by internal errors are imperative. The goals of this study w...Show More
Resembled Tactile Feedback for Object Recognition Using a Prosthetic Hand
Tactile feedback in the hand is essential for interaction with objects. Here, we evaluated how artificial tactile sensation affected the recognition of object properties using a myoelectrically controlled prosthetic hand. Electromyogram signals from the flexor and extensor finger muscles were used to continuously control either prosthetic joint velocity or position. Participants grasped objects of...Show More
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