User Controlled Interface for Tuning Robotic Knee Prosthesis

Alili, Abbas; Nalam, Varun; Li, Minhan; Liu, Ming; Si, Jennie; Huang, He

doi:10.1109/iros51168.2021.9636264

Cited by 11 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since our RL tuning algorithm was time-efficient and the learned prosthesis tuning policy was robust across various kinematic profiles, this framework offered us a unique opportunity to investigate the user's preferences across various knee prosthesis control parameters. Our pilot testing showed the design and feasibility of this engineering framework in adjusting stance phase knee features [31].…”

Section: Introductionmentioning

confidence: 93%

“…To address this challenge and enable the tuning of 12 knee prosthesis control parameters to meet the user's preference while ensuring user safety and minimizing human exertion, we proposed a novel hierarchical engineering framework consisting of (1) our previously developed RL-based prosthesis tuning method [16] and (2) a User Controlled Interface (UCI) [31]. The basic working mechanism was that the human users can use the high-level UCI to determine their own preferred prosthesis knee kinematic features in a 4-dimensional space by modifying control points which is then realized by the low-level RL-based tuning algorithm adjusting all 12 impedance control parameters to meet the desired kinematic features.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis

Alili

Nalam

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

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 propose and evaluate a novel prosthesis control tuning framework for a robotic knee prosthesis, which could enable user preferred robot behavior in the device tuning process. The framework consists of 1) a User-Controlled Interface that allows the user to select their preferred knee kinematics in gait and 2) a reinforcement learning-based algorithm for tuning high-dimension prosthesis control parameters to meet the desired knee kinematics. We evaluated the performance of the framework along with usability of the developed user interface. In addition, we used the developed framework to investigate whether amputee users can exhibit a preference between different profiles during walking and whether they can differentiate between their preferred profile and other profiles when blinded. The results showed effectiveness of our developed framework in tuning 12 robotic knee prosthesis control parameters while meeting the user-selected knee kinematics. A blinded comparative study showed that users can accurately and consistently identify their preferred prosthetic control knee profile. Further, we preliminarily examined gait biomechanics of the prosthesis users when walking with different prosthesis control and did not find clear difference between walking with preferred prosthesis control and when walking with normative gait control parameters. This study may inform future translation of this novel prosthesis tuning framework for home or clinical use.

show abstract

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis

Alili

Nalam

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

show abstract

“…It is understandable given that the prosthesis involves a lot of parameters to be tuned and can be complicated to manage without sufficient training. Recently, a new trending line of research made this possible by developing a prosthesis autotuning system and interface (i.e., User Controlled Interface) that can be operated by the users (Alili et al, 2021;Li et al, 2021). The auto-tuning system simplified the tuning process with the support of the tuning algorithm and defined only four control points of the prosthesis knee joint to be adjusted by a user.…”

Section: Preferences For Prosthesismentioning

confidence: 99%

“…Recently, there have been trending efforts on user-guided prosthesis auto-tuning to improve the efficiency (e.g., Alili et al, 2021;Thatte et al, 2017). However, modern prostheses can have a lot of control parameters (e.g., nine for OttoBack).…”

Section: Introductionmentioning

confidence: 99%

Understanding the Preferences for Lower-Limb Prosthesis: A Think-Aloud Study during User-Guided Auto-Tuning

Yuan

Bai

Alili

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

Self Cite

View full text Add to dashboard Cite

Prostheses help amputees to maintain physical health and quality of life. Prosthesis wearers’ satisfaction and adherence to the prosthesis are closely related to the preferences for prosthesis tuning settings. However, the underlying factors that contribute to the preferences were under-explored. In this study, two able-bodied participants were asked to change the robotic prosthesis settings to their preferred state and the think-aloud technique with a mixed-method approach was used to reveal the contributing factors of preferences. We found that physical perception (e.g., positions of the prosthetic foot, balance, and stability) and subjective feelings (e.g., comfortableness, satisfaction, confidence, and worries) were two major factors. Experiences with the intact leg and other profiles were used as anchors for their preference levels. Preferences may also differ with situational context such as walking speed. The saturation points were reached with no strong approach motivation. The implications for prosthesis design and research were discussed.

show abstract

“…As devices get more complex, the control and tuning often requires the technical knowledge of an engineer to translate the clinician's prescription into control parameters [11] , [13] , [14] . Researchers are working on this problem by building tuning interfaces that allow a clinician or user to directly tune the device without an engineer [15] , [16] , [17] . The decision-making process of clinicians has also been encoded into auto-tuning algorithms for robotic prosthetic leg controllers [18] , [19] .…”

Section: Introductionmentioning

confidence: 99%

Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing

Reznick

Welker

Gregg

2022

IEEE Open J. Eng. Med. Biol.

View full text Add to dashboard Cite

Accounting for gait individuality is important to positive outcomes with wearable robots, but manually tuning multi-activity models is time-consuming and not viable in a clinic. Generalizations can possibly be made to predict gait individuality in unobserved conditions. METHODS: Kinematic individuality-how one person's joint angles differ from the group-is quantified for every subject, joint, ambulation mode (walking, running, stair ascent, and stair descent), and intramodal task (speed, incline) in an open-access dataset with 10 able-bodied subjects. Four N-way ANOVAs test how prediction methods affect the fit to experimental data between and within ambulation modes. We test whether walking individuality (measured at a single speed on level ground) carries across modes, or whether a mode-specific prediction (based on a single task for each mode) is significantly more effective. RESULTS: Kinematic individualization improves fit across joint and task if we consider each mode separately. Across all modes, tasks, and joints, modal individualization improved the fit in 81% of trials, improving the fit on average by 4.3°across the gait cycle. This was statistically significant at all joints for walking and running, and half the joints for stair ascent/descent. CONCLUSIONS: For walking and running, kinematic individuality can be easily generalized within mode, but the trends are mixed on stairs depending on joint.

show abstract

User Controlled Interface for Tuning Robotic Knee Prosthesis

Cited by 11 publications

References 18 publications

A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis

A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis

Understanding the Preferences for Lower-Limb Prosthesis: A Think-Aloud Study during User-Guided Auto-Tuning

Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing

Contact Info

Product

Resources

About