@article{liu_naseri_lee_hu_lewek_huang_2023, title={A simplified model for whole-body angular momentum calculation}, volume={111}, ISSN={["1873-4030"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85144824437&partnerID=MN8TOARS}, DOI={10.1016/j.medengphy.2022.103944}, abstractNote={The capability to monitor gait stability during everyday life could provide key information to guide clinical intervention to patients with lower limb disabilities. Whole body angular momentum (Lbody) is a convenient stability indicator for wearable motion capture systems. However, Lbody is costly to estimate, because it requires monitoring all major body segment using expensive sensor elements. In this study, we developed a simplified rigid body model by merging connected body segments to reduce the number of body segments, which need to be monitored. We demonstrated that the Lbody could be estimated by a seven-segment model accurately for both people with and without lower extremity amputation.}, journal={MEDICAL ENGINEERING & PHYSICS}, author={Liu, Ming and Naseri, Amirreza and Lee, I-Chieh and Hu, Xiaogang and Lewek, Michael D. and Huang, He}, year={2023}, month={Jan} }
 @article{naseri_liu_lee_huang_2023, title={Development and Online Validation of an Intrinsic Fault Detector for a Powered Robotic Knee Prosthesis}, ISSN={["2153-0858"]}, DOI={10.1109/IROS55552.2023.10342433}, abstractNote={Robotic prosthetic legs have the potential to significantly improve the quality of life for lower limb amputees to perform locomotion in various environments and task conditions. However, these devices lack the capability to recover from internal intrinsic control faults, which can lead to harmful consequences affecting the user's gait performance and eroding trust in these robotic devices. Therefore, a reliable fault detection system is necessary to detect intrinsic faults in a timely manner and provide a compensatory response to mitigate their effects. This paper focuses on designing an active fault detector for a robotic knee prosthesis and demonstrates its effectiveness in real time. The developed system utilizes a Gaussian Process model to estimate knee angular velocity, which is sensitive to intrinsic faults and relies on the difference between estimated velocity and the actual measurement to detect internal control faults. In an offline analysis, the developed detector demonstrated a higher detection rate, lower false alarm ratio, and faster detection time compared with the two approaches reported previously. An online demonstration was also conducted with a unilateral amputee participant and showed performance similar to that of offline analysis. We expect that this detector can be integrated into a fault tolerance strategy to enhance the reliability and safety of robotic prosthetic legs, enabling users to perform their everyday tasks with greater confidence.}, journal={2023 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, IROS}, author={Naseri, Amirreza and Liu, Ming and Lee, I-Chieh and Huang, Helen}, year={2023}, pages={2158–2164} }
 @article{naseri_lee_huang_liu_2023, title={Investigating the Association of Quantitative Gait Stability Metrics With User Perception of Gait Interruption Due to Control Faults During Human-Prosthesis Interaction}, volume={31}, ISSN={["1558-0210"]}, DOI={10.1109/TNSRE.2023.3328877}, abstractNote={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 difficult to acquire online. The quantitative gait stability metrics can be objectively measured and monitored using wearable sensors; however, objective measurements of gait stability associated with human’s perception of their own gait stability has rarely been reported. By identifying quantitative measurements that associate with users’ perceptions, we can gain a more accurate and comprehensive understanding of an individual’s perceived functional outcomes of assistive devices such as prostheses. To achieve our research goal, experiments were conducted to artificially apply internal disturbances in the powered prosthesis while the prosthetic users performed level ground walking. We monitored and compared multiple gait stability metrics and a local measurement to the users’ reported perception of their own gait stability. The results showed that the center of pressure progression in the sagittal plane and knee momentum (i.e., residual thigh and prosthesis shank angular momentum about prosthetic knee joint) can potentially estimate the users’ perceptions of gait stability when experiencing disturbances. The findings of this study can help improve the development and evaluation of gait stability control algorithms in robotic prosthetic devices.}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Naseri, Amirreza and Lee, I-Chieh and Huang, He and Liu, Ming}, year={2023}, pages={4693–4702} }
 @article{naseri_liu_lee_liu_huang_2022, title={Characterizing Prosthesis Control Fault During Human-Prosthesis Interactive Walking Using Intrinsic Sensors}, volume={7}, ISSN={["2377-3766"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85133795882&partnerID=MN8TOARS}, DOI={10.1109/LRA.2022.3186503}, abstractNote={The physical interactions between wearable lower limb robots and humans have been investigated to inform effective robot design for walking augmentation. However, human-robot interactions when internal faults occur within robots have not been systematically reported, but it is essential to improve the robustness of robotic devices and ensure the user’s safety. This letter aims to (1) present a methodology to characterize the behavior of the robotic transfemoral prosthesis as an effective wearable robot platform while interacting with the users in the presence of internal faults, and (2) identify the potential data sources for accurate detection of the prosthesis fault. We first obtained the human perceived response in terms of their walking stability when the prosthesis control fault (inappropriate intrinsic control output/command) was emulated/applied in level-ground walking. Then the measurements and their features, obtained from the transfemoral prosthesis, were examined for the emulated faults that elicited a sense of instability in human users. The optimal features that contributed the most in separating faulty interaction from the normal walking condition were determined using two machine-learning-based approaches: One-Class Support Vector Machine (OCSVM) and Mahalanobis Distance (MD) classifier. The OCSVM anomaly detector could achieve an average sensitivity of 85.7% and an average false alarm rate of 1.7% with a reasonable detecting time of 147.6 ms for detecting emulated control errors among all subjects. The result demonstrates the potential of using machine-learning-based schemes in identifying prosthesis control faults based on intrinsic sensors on the prosthesis. This study presents a procedure to study human-robot fault tolerance and inform the future design of robust prosthesis control.}, number={3}, journal={IEEE ROBOTICS AND AUTOMATION LETTERS}, author={Naseri, Amirreza and Liu, Ming and Lee, I-Chieh and Liu, Wentao and Huang, He}, year={2022}, month={Jul}, pages={8307–8314} }