Amputees’ preferences for prosthesis settings are critical not only for their psychological well-being but also for long-term adherence to device adoption and health. Although active lower-limb prostheses can provide enhanced functionality than passive devices, little is known about the mechanism of preferences for settings in active devices. Therefore, a think-aloud study was conducted on three amputees to unravel their preferences for a powered robotic knee prosthesis during user-guided auto-tuning. The inductive thematic analysis revealed that amputee patients were more likely to use their own passive device rather than the intact leg as the reference for the natural walking that they were looking for in the powered device. There were large individual differences in factors influencing naturalness. The mental optimization of preference decisions was mostly based on the noticeableness of the differences between knee profiles. The implications on future design and research in active prostheses were discussed.
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.
more » « less- Award ID(s):
- 1926998
- NSF-PAR ID:
- 10482756
- Publisher / Repository:
- Sage Journals
- Date Published:
- Journal Name:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Volume:
- 66
- Issue:
- 1
- ISSN:
- 2169-5067
- Page Range / eLocation ID:
- 2159 to 2163
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Background Transfemoral prosthesis users’ high fall rate is related to increased injury risk, medical costs, and fear of falling. Better understanding how stumble conditions (e.g., participant age, prosthesis type, side tripped, and swing phase of perturbation) affect transfemoral prosthesis users could provide insight into response deficiencies and inform fall prevention interventions.
Methods Six unilateral transfemoral prosthesis users experienced obstacle perturbations to their sound limb in early, mid, and late swing phase. Fall outcome, recovery strategy, and kinematics of each response were recorded to characterize (1) recoveries versus falls for transfemoral prosthesis users and (2) prosthesis user recoveries versus healthy adult recoveries.
Results Out of 26 stumbles, 15 resulted in falls with five of six transfemoral prosthesis users falling at least once. By contrast, in a previously published study of seven healthy adults comprising 214 stumbles using the same experimental apparatus, no participants fell. The two oldest prosthesis users fell after every stumble, stumbles in mid swing resulted in the most falls, and prosthesis type was not related to strategy/fall outcomes. Prosthesis users who recovered used the elevating strategy in early swing, lowering strategy in late swing, and elevating or lowering/delayed lowering with hopping in mid swing, but exhibited increased contralateral (prosthetic-side) thigh abduction and trunk flexion relative to healthy controls. Falls occurred if the tripped (sound) limb did not reach ample thigh/knee flexion to sufficiently clear the obstacle in the elevating step, or if the prosthetic limb did not facilitate a successful step response after the initial sound-side elevating or lowering step. Such responses generally led to smaller step lengths, less anterior foot positioning, and more forward trunk flexion/flexion velocity in the resulting foot-strikes.
Conclusions Introducing training (e.g., muscle strength or task-specific motor skill) and/or modifying assistive devices (e.g., lower-limb prostheses or exoskeletons) may improve responses for transfemoral prosthesis users. Specifically, training or exoskeleton assistance could help facilitate sufficient thigh/knee flexion for elevating; training or prosthesis assistance could provide support-limb counteracting torques to aid in elevating; and training or prosthesis assistance could help initiate and safely complete prosthetic swing.
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Although the average healthy adult transitions from sit to stand over 60 times per day, most research on powered prosthesis control has only focused on walking. In this paper, we present a data-driven controller that enables sitting, standing, and walking with minimal tuning. Our controller comprises two high level modes of sit/stand and walking, and we develop heuristic biomechanical rules to control transitions. We use a phase variable based on the user's thigh angle to parameterize both walking and sit/stand motions, and use variable impedance control during ground contact and position control during swing. We extend previous work on data-driven optimization of continuous impedance parameter functions to design the sit/stand control mode using able-bodied data. Experiments with a powered knee-ankle prosthesis used by a participant with above-knee amputation demonstrate promise in clinical outcomes, as well as trade-offs between our minimal-tuning approach and accommodation of user preferences. Specifically, our controller enabled the participant to complete the sit/stand task 20% faster and reduced average asymmetry by half compared to his everyday passive prosthesis. The controller also facilitated a timed up and go test involving sitting, standing, walking, and turning, with only a mild (10%) decrease in speed compared to the everyday prosthesis. Our sit/stand/walk controller enables multiple activities of daily life with minimal tuning and mode switching.more » « less
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Abstract Background Human-human (HH) interaction mediated by machines (e.g., robots or passive sensorized devices), which we call human-machine-human (HMH) interaction, has been studied with increasing interest in the last decade. The use of machines allows the implementation of different forms of audiovisual and/or physical interaction in dyadic tasks. HMH interaction between two partners can improve the dyad’s ability to accomplish a joint motor task (
task performance ) beyond either partner’s ability to perform the task solo. It can also be used to more efficiently train an individual to improve their solo task performance (individual motor learning ). We review recent research on the impact of HMH interaction on task performance and individual motor learning in the context of motor control and rehabilitation, and we propose future research directions in this area.Methods A systematic search was performed on the Scopus, IEEE Xplore, and PubMed databases. The search query was designed to find studies that involve HMH interaction in motor control and rehabilitation settings. Studies that do not investigate the effect of changing the interaction conditions were filtered out. Thirty-one studies met our inclusion criteria and were used in the qualitative synthesis.
Results Studies are analyzed based on their results related to the effects of interaction type (e.g., audiovisual communication and/or physical interaction), interaction mode (collaborative, cooperative, co-active, and competitive), and partner characteristics. Visuo-physical interaction generally results in better dyadic task performance than visual interaction alone. In cases where the physical interaction between humans is described by a spring, there are conflicting results as to the effect of the stiffness of the spring. In terms of partner characteristics, having a more skilled partner improves dyadic task performance more than having a less skilled partner. However, conflicting results were observed in terms of individual motor learning.
Conclusions Although it is difficult to draw clear conclusions as to which interaction type, mode, or partner characteristic may lead to optimal task performance or individual motor learning, these results show the possibility for improved outcomes through HMH interaction. Future work that focuses on selecting the optimal personalized interaction conditions and exploring their impact on rehabilitation settings may facilitate the transition of HMH training protocols to clinical implementations.
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Abstract Odorous compound emissions and odor complaints from the public are rising concerns for agricultural, industrial, and water resource recovery facilities (WRRFs) near urban areas. Many facilities are deploying sensors that measure malodorous compounds and other factors related to odor creation and dispersion. Focusing on the Metropolitan Water Reclamation District of Greater Chicago's (MWRDGCs) Thornton Composite Reservoir (7.9 billion gallon capacity), we used meteorological, operational, and H2S sensor data to train a 3‐day advance‐warning predictor of local odor complaints, so as to implement targeted odor prevention measures. Using a machine learning approach, we bypassed difficulties in modeling both physical dispersion and human perception of odors. Utilizing random forest algorithms with varied settings and input attributes, we find that a small network of H2S sensors, meteorological data, and operational data are able to predict odor complaints three days in advance with greater than 60% accuracy and less than 25% false‐positive rates, exceeding MWRDGC’s standards required for full‐scale deployment.
Practitioner points A random forest algorithm trained on H2S, weather, and operations data successfully predicted odor complaints surrounding a large composite reservoir.
Thirty‐two data attribute combinations were tested. It was found that H2S sensor data alone are insufficient for predicting odor complaints.
The best predictor was a Random Forest Classifier trained on weather, operational, and H2S readings from the reservoir corner locations.
This study demonstrates odor complaint prediction capability utilizing a limited set of data sources and open‐source machine learning techniques.
Given a small network of H2S sensors and organized data management, WRRFs and similar facilities can conduct advance‐warning odor complaint prediction.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/1071181322661082
