Characterization of Human Shoulder Joint Stiffness Across 3D Arm Postures and Its Sex Differences.

Understanding the characteristics of shoulder joint stiffness can offer insights into how the shoulder joint contributes to arm stability and assists in various arm postures and movements. This study aims to characterize posture-dependent shoulder stiffness in a three-dimensional (3D) space and investigate its potential sex differences. A multi-degree-of-freedom, parallel-actuated shoulder exoskeleton robot was used' to perturb the participant's shoulder joint and measure the resulting torque responses while participants relaxed their shoulder muscles. The group average results of 40 healthy individuals (20 males and 20 females) revealed that arm postures significantly affect shoulder stiffness, particularly in postures involving shoulder flexion/extension and horizontal flexion/extension. Shoulder stiffness consistently increased as the shoulder flexion angle decreased and the shoulder horizontal flexion/extension approached the limit of its range of motion. The comparative group results between males and females indicated that shoulder stiffness in males was greater than that in females across all 15 arm postures measured in this study. Even after normalizing the data by subject body mass, the female group showed significantly lower stiffness than the male group in 12 out of the 15 arm postures. The results highlight that 3D arm postures and sex significantly affect shoulder stiffness even under relaxed muscles. This study provides valuable foundations for future studies aimed at characterizing shoulder stiffness in the context of active muscles and dynamic movement tasks, evaluating changes in shoulder stiffness following neuromuscular injuries, and formulating rehabilitative training protocols for individuals suffering from shoulder problems.

Reduced trunk movement control during motor dual-tasking in older adults.

Older adults have a decreased trunk movement control which is linked to their higher fall risk. While motor/cognitive dual-tasking deteriorates balance and walking in older adults, there is limited understanding on how trunk kinematics and kinetics are affected by dual-tasking in scenarios where falls can occur. Therefore, the purpose of the study was to determine the impacts of a challenging motor dual-task, specifically obstacle avoidance during walking, on trunk and lower-body kinematics and kinetics of older adults compared to young adults. The study captured three-dimensional kinematic and kinetic data from 12 young adults and 10 older adults as they walked on a treadmill and stepped over an obstacle with both legs. The study analyzed trunk, hip, knee, and ankle angles and torques. Trunk torque was further broken down to trunk muscle torque, gravitational torque, and inertia torque. A linear mixed effects model was used to investigate the difference in each variable between the two groups. Older adults exhibited significantly increased trunk flexion angle and trunk extension muscle torque compared to young adults, with the trunk being the only segment/joint showing differences in both kinematics and kinetics. Trunk torque breakdown analysis revealed that larger trunk flexion led to a larger gravitational torque, which contributed to an increased compensatory trunk muscle torque. Moreover, older adults' less controlled trunk flexion during weight shifting from trail leg to the lead leg, necessitated a compensatory trunk deceleration during trail leg obstacle avoidance which was achieved by generating additional increase in trunk muscle torque. The study demonstrated that motor dual-tasking has the most negative effects on trunk control in older adults compared to young adults. This exposes older adults to a higher fall risk. Therefore, future work should focus on supporting trunk control during daily multi-tasking conditions where falls can occur.

Design of a wearable shoulder exoskeleton robot with dual-purpose gravity compensation and a compliant misalignment compensation mechanism.

This paper presents the design and validation of a wearable shoulder exoskeleton robot intended to serve as a platform for assistive controllers that can mitigate the risk of musculoskeletal disorders seen in workers. The design features a four-bar mechanism that moves the exoskeleton's center of mass from the upper shoulders to the user's torso, dual-purpose gravity compensation mechanism located inside the four-bar's linkages that supports the full gravitational loading from the exoskeleton with partial user's arm weight compensation, and a novel 6 degree-of-freedom (DoF) compliant misalignment compensation mechanism located between the end effector and the user's arm to allow shoulder translation while maintaining control of the arm's direction. Simulations show the four-bar design lowers the center of mass by  cm and the kinematic chain can follow the motion of common upper arm trajectories. Experimental tests show the gravity compensation mechanism compensates gravitational loading within  Nm over the range of shoulder motion and the misalignment compensation mechanism has the desired 6 DoF stiffness characteristics and range of motion to adjust for shoulder center translation. Finally, a workspace admittance controller was implemented and evaluated showing the system is capable of accurately reproducing simulated impedance behavior with transparent low-impedance human operation.

Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data.

Deep learning methods have achieved a lot of success in various applications involving converting wearable sensor data to actionable health insights. A common application areas is activity recognition, where deep-learning methods still suffer from limitations such as sensitivity to signal quality, sensor characteristic variations, and variability between subjects. To mitigate these issues, robust features obtained by topological data analysis (TDA) have been suggested as a potential solution. However, there are two significant obstacles to using topological features in deep learning: (1) large computational load to extract topological features using TDA, and (2) different signal representations obtained from deep learning and TDA which makes fusion difficult. In this paper, to enable integration of the strengths of topological methods in deep-learning for time-series data, we propose to use two teacher networks - one trained on the raw time-series data, and another trained on persistence images generated by TDA methods. These two teachers are jointly used to distill a single student model, which utilizes only the raw time-series data at test-time. This approach addresses both issues. The use of KD with multiple teachers utilizes complementary information, and results in a compact model with strong supervisory features and an integrated richer representation. To assimilate desirable information from different modalities, we design new constraints, including orthogonality imposed on feature correlation maps for improving feature expressiveness and allowing the student to easily learn from the teacher. Also, we apply an annealing strategy in KD for fast saturation and better accommodation from different features, while the knowledge gap between the teachers and student is reduced. Finally, a robust student model is distilled, which can at test-time uses only the time-series data as an input, while implicitly preserving topological features. The experimental results demonstrate the effectiveness of the proposed method on wearable sensor data. The proposed method shows 71.74% in classification accuracy on GENEActiv with WRN16-1 (1D CNNs) student, which outperforms baselines and takes much less processing time (less than 17 sec) than teachers on 6k testing samples.

Human Gait Entrainment to Soft Robotic Hip Perturbation During Simulated Overground Walking.

Entraining human gait with a periodic mechanical perturbation has been proposed as a potentially effective strategy for gait rehabilitation, but the related studies have mostly depended on the use of a fixed-speed treadmill (FST) due to various practical constraints. However, imposing a constant treadmill speed on participants becomes a critical problem because this speed constraint prohibits the participants from adjusting the gait speed, resulting in significant alterations in natural biomechanics as the entrainment alters the stride frequency. In this study, we hypothesized that the use of a variable-speed treadmill (VST), which enables the participants to continuously adjust their speed, can improve the success rate of gait entrainment and preserve natural gait biomechanics. To test this hypothesis, we recruited 15 young and healthy adults and let them walk on a conventional FST and a self-paced VST while wearing a soft robotic hip exosuit, which applied hip flexion perturbations at various frequencies, ranging from the preferred walking frequency to a 30% increased value. Kinematics and kinetics of the participants' walking under the two treadmill conditions were measured on two separate days. Experimental results demonstrated a higher success rate of entrainment during VST walking compared to FST walking, particularly at faster perturbation frequencies. Furthermore, walking on VST facilitated the maintenance of natural biomechanics, such as stride length and normalized propulsive impulse, better than walking on FST. The observed improvement, primarily attributed to allowing an increase in walking speed following the increase in the perturbation frequency, suggests that using a self-paced VST is a viable method for exploiting the potentially beneficial therapeutic effects of entrainment in gait rehabilitation.

Modern smartphone usage can negatively impact postural balance while standing on dynamically challenging grounds.

BACKGROUND: While several studies have explored the impacts of smartphone usage on postural balance, their tasks are limited to texting or calling, and the studies were performed on rigid ground. RESEARCH QUESTIONS: METHODS: Sixteen healthy young adults were recruited to perform two smartphone tasks: taking selfies and posting statuses on social media; participants were standing on four different grounds: rigid, foam-based compliant, robot-simulated compliant, and robot-simulated oscillatory grounds. The center-of-pressure (CoP) under each foot was recorded via force plates and the net CoP was calculated. Temporal, spatial, and control aspects of postural balance were analyzed by virtual time-to-contact (VTC), CoP path length (PL) and sway area (SA), and switching rate (SR), respectively. Two-way repeated measures analysis of variance (ANOVA) tests were performed for each dependent variable to compare the mean differences between smartphone tasks and ground conditions and their interaction effect. Paired t-tests with Bonferroni correction were used to determine significant differences in post-hoc analyses. RESULTS: VTC decreased significantly whereas CoP PL and SA increased significantly during smartphone usage (all p-values <0.001). Interaction effects between task and ground condition (all p-values <0.001) were observed in all measures but SR, implying that the effect of smartphone usage on postural balance can significantly change depending on the ground condition. SIGNIFICANCE: These results highlight the potential fall risks due to the impact of modern smartphone usage on standing balance. Understanding the effect of smartphone usage on standing balance and the interaction effect with various ground conditions opens the door for potential balance assistive devices and mobile phone applications to minimize falls.

Characterizing postural balance on 2-dimensional compliant surfaces with directional virtual time-to-contact.

BACKGROUND: This study aimed to (1) investigate postural balance control on 2-Dimensional (2D) compliant surfaces using directional virtual time-to-contact (d-VTC), a novel method for VTC calculation; and (2) compare d-VTC with conventional balance measures in this context. METHODS: A dual-axis robotic platform was used to simulate 2D surfaces/grounds with varying compliance levels. Twenty healthy young adults stood on the platform with either open or closed eyes. Balance was evaluated using d-VTC in multiple aspects, including temporal (VTC mean), spatial (boundary contact - BC), and control aspects (switching rate - SR). Additionally, conventional balance measures, namely center-of-pressure (COP) area and COP root-mean-square (RMS), were employed for further comparisons with d-VTC measures. Normality checks were performed using Shapiro-Wilk tests. Two-way repeated measures ANOVA tests were used to examine the effects of surface compliance and vision on postural balance, followed by post-hoc pairwise comparisons across conditions with Bonferroni correction. RESULTS: The results showed that increasing surface compliance and/or absence of vision caused a significant decrease in VTC mean (all p-values <0.001; all ηp2 > 0.816). Interaction effects between surface compliance and vision on 2D and ML VTC mean were also significant (all p-values <0.019; all ηp2 > 0.355). The AP and ML BC values indicated a converging trend to 50%. No vision effect was observed (p = 0.458), but both surface compliance (p = 0.001; ηp2 = 0.522) and interaction (p = 0.002; ηp2 = 0.492) effects were significant. Decreases in SR were significant due to the compliance of the standing surface (p = 0.01; ηp2 = 0.401) but not vision (p = 0.109). COP area increased due to both surface and vision conditions (all p-values <0.001; all ηp2 > 0.872). AP and ML RMS were altered by vision (all p-values <0.001; all ηp2 > 0.741), but not by surface condition (all p-values >0.06). No interaction effect was observed in the conventional measures (all p-values >0.07). CONCLUSION: Balance control is compromised by 2D compliant surfaces, which is exacerbated when vision is absent. Among all balance measures, VTC mean measures demonstrated particularly high sensitivity in identifying decreased balance capabilities, while BC and SR provided new insights into fall risks and balance control mechanisms. These insights may facilitate the development of rehabilitation training or assistive devices for fall prevention.

Introduction to a Twin Dual-Axis Robotic Platform for Studies of Lower Limb Biomechanics.

This paper presents a twin dual-axis robotic platform system which is designed for the characterization of postural balance under various environmental conditions and quantification of bilateral ankle mechanics in 2 degrees-of-freedom (DOF) during standing and walking. Methods: Validation experiments were conducted to evaluate performance of the system: 1) to apply accurate position perturbations under different loading conditions; 2) to simulate a range of stiffness-defined mechanical environments; and 3) to reliably quantify the joint impedance of mechanical systems. In addition, several human experiments were performed to demonstrate the system's applicability for various lower limb biomechanics studies. The first two experiments quantified postural balance on a compliance-controlled surface (passive perturbations) and under oscillatory perturbations with various frequencies and amplitudes (active perturbations). The second two experiments quantified bilateral ankle mechanics, specifically, ankle impedance in 2-DOF during standing and walking. The validation experiments showed high accuracy of the platform system to apply position perturbations, simulate a range of mechanical environments, and quantify the joint impedance. Results of the human experiments further demonstrated that the platform system is sensitive enough to detect differences in postural balance control under challenging environmental conditions as well as bilateral differences in 2-DOF ankle mechanics. This robotic platform system will allow us to better understand lower limb biomechanics during functional tasks, while also providing invaluable knowledge for the design and control of many robotic systems including robotic exoskeletons, prostheses and robot-assisted balance training programs. Clinical and Translational Impact Statement- Our robotic platform system serves as a tool to better understand the biomechanics of both healthy and neurologically impaired individuals and to develop assistive robotics and rehabilitation training programs using this information.

Directional virtual time-to-contact: A new measure for investigating temporal, spatial, and control aspects of postural balance control.

Virtual time-to-contact (VTC) is a promising approach for investigating postural balance control. However, current VTC calculation approaches are limited as they (1) cannot be used to evaluate directional components of balance, and (2) only assess a single, temporal aspect of balance control. This study introduces a new approach for VTC calculation, namely directional VTC, expanding VTC to assess temporal, spatial, and control aspects of balance. Three case studies were conducted across varying populations and conditions as a proof-of-concept of the presented method. The first study examined quiet stance on a firm surface in people with Parkinson's disease (PD; n = 10) in comparison to their healthy peers (n = 10). The second and third studies assessed balance control of healthy individuals under challenging environments. Ten healthy individuals participated in standing tasks on compliant ground surfaces, while another ten on oscillatory ground surfaces, all simulated by a dual-axis robotic platform. Preliminary results not only provided a closer look at balance control with multiple aspects, including temporal, spatial, and control aspects, but also showed how different aspects of balance changed due to neurological diseases (Case Study I) or challenging standing grounds (Case Studies II and III). This study advances our understanding of posture biomechanics and its clinical applications.

Multi-tasking deteriorates trunk movement control during and after obstacle avoidance.

Dynamic and cognitive multi-tasking might affect balance and walking negatively and increase risk of falling. Trunk movement control is critical for balance maintenance and fall-prevention. The impact of multi-tasking on trunk movement control has not been thoroughly studied. In a challenging dynamic multi-tasking condition such as walking and obstacle avoidance, presence of a cognitive task not only increases risk of tripping but also may increase risk of falling by deteriorating trunk control. Our objective was to investigate the impacts of a challenging dynamic and cognitive multi-tasking condition (walking + obstacle avoidance + cognitive task) on trunk kinematics and kinetics and compare those with other joints/segments. Trunk, pelvis, hip, knee, and ankle kinematics and kinetics of 12 young adults were compared between joints/segments and conditions. During walking and obstacle avoidance (dynamic multi-tasking), the trunk had the largest normalized increase in peak flexion angle and extension torque compared to walking, among the other joints/segments. The presence of a cognitive task during walking and obstacle avoidance (dynamic and cognitive multi-tasking) did not impact any of the joints/segments biomechanics except the trunk peak extension torque that was increased. Furthermore, trunk kinematics showed the largest residual differences (post-effects) in 3 cycles after obstacle avoidance compared to walking. The presence of a cognitive task (dynamic and cognitive multi-tasking) did not impact the post-effects of obstacle avoidance on any joints/segments except the trunk with its residual difference from normal walking further increased. These results suggest that a cognitive task deteriorates trunk control and interferes with the ability to regain normal trunk biomechanics after obstacle avoidance. In summary, the trunk requires the largest biomechanical adjustments in a challenging dynamic and cognitive multi-tasking condition where there is a risk of falling. Our study provides baseline results suggesting that trunk control demands more attention and is more negatively affected by dynamic and cognitive multi-tasking. Our results raise a concern for elderly population as their trunk control is already impaired and common daily multi-tasking could further deteriorate their trunk control and increase fall risk.

Concurrent locomotor adaptation and retention to visual and split-belt perturbations.

Gait asymmetry is a common symptom in groups with neurological disorders and significantly reduces gait efficiency. To develop efficient training for gait rehabilitation, we propose a novel gait rehabilitation paradigm that combines two distinct perturbation strategies: visual feedback distortion (VFD) and split-belt treadmill (SBT) walking. In SBT walking, spatiotemporal gait adaptation can be readily achieved, but it quickly fades after training. Gait adaptation to implicit VFD in an unconscious manner tends to persist longer, potentially due to a greater engagement of implicit learning during training. Thus, we investigated whether the combined strategies would lead to more effective changes in symmetric gait patterns with longer retention periods. We compared the retention of the preserved asymmetric pattern acquired by "implicit VFD+SBT walking" with "SBT-only walking" and with "SBT walking with conscious correction". In the implicit VFD+SBT walking, the speed of the two belts was gradually changed, the visual representation of gait symmetry was implicitly distorted, and no instructions were given to subjects except to watch the visual feedback. In the SBT walking with conscious correction, subjects were instructed to consciously correct their steps with the help of visual feedback while SBT walking. The SBT-only walking consisted of SBT walking with no visual feedback. After the 7-minute adaptation period, we removed the visual feedback and the split-belt perturbations, and we assessed the retention of the preserved asymmetric pattern while subjects continued walking for the 15-minute post-adaptation period. In a group of subjects who spontaneously showed visuomotor adaptation in response to the implicit VFD (16 out of 27 subjects), we found a greater retention rate during the implicit VFD+SBT walking trial than the SBT-only walking or the SBT walking with conscious correction trials. The implicit visual distortion paradigm delivered in an attention-independent (unconscious) manner can be utilized and integrated into SBT walking to improve the efficacy of symmetric gait adaptation by producing longer-lasting effects on the retention of a newly learned motor pattern.

Development and application of survey-based artificial intelligence for clinical decision support in managing infectious diseases: A pilot study on a hospital in central Vietnam.

Introduction: In this study, we developed a simplified artificial intelligence to support the clinical decision-making of medical personnel in a resource-limited setting. Methods: We selected seven infectious disease categories that impose a heavy disease burden in the central Vietnam region: mosquito-borne disease, acute gastroenteritis, respiratory tract infection, pulmonary tuberculosis, sepsis, primary nervous system infection, and viral hepatitis. We developed a set of questionnaires to collect information on the current symptoms and history of patients suspected to have infectious diseases. We used data collected from 1,129 patients to develop and test a diagnostic model. We used XGBoost, LightGBM, and CatBoost algorithms to create artificial intelligence for clinical decision support. We used a 4-fold cross-validation method to validate the artificial intelligence model. After 4-fold cross-validation, we tested artificial intelligence models on a separate test dataset and estimated diagnostic accuracy for each model. Results: We recruited 1,129 patients for final analyses. Artificial intelligence developed by the CatBoost algorithm showed the best performance, with 87.61% accuracy and an F1-score of 87.71. The F1-score of the CatBoost model by disease entity ranged from 0.80 to 0.97. Diagnostic accuracy was the lowest for sepsis and the highest for central nervous system infection. Conclusion: Simplified artificial intelligence could be helpful in clinical decision support in settings with limited resources.

Influence of task complexity on movement planning and release after stroke: insights from startReact.

The capacity to plan movement following stroke is diminished when reaching from a standing position. Two mechanisms have been proposed: increased task complexity compared to simpler tasks and inhibition between the pathways controlling whole-body posture and upper extremity reaching. The objective of this study was to determine if task complexity alone can alter planning and release (or involuntary execution) capacity when whole-body postural adjustment is not required. Data were collected from 10 stroke survivors and 8 age-matched controls. Ballistic elbow extension movements were performed with and without voluntary shoulder abduction, adding complexity by anti-gravity arm support that enhanced the expression of abnormal muscle synergies linking elbow and shoulder after stroke. Our primary finding was in support of our hypothesis that startReact (involuntary release of planned movement by a startling stimulus) would be intact but that the increased task complexity would decrease the capacity to plan and release movement. StartReact was intact for both tasks with and without shoulder abduction. Despite the intact startReact response across both conditions following stroke, the incidence of startReact was decreased during the shoulder abduction task similar to prior studies showing a decrease during tasks of higher complexity. Our results suggest that individuals with stroke have a diminished capacity to plan and release movement as task complexity increases. This study highlights the unique potential for startReact to be used as a clinical tool to probe the capacity to plan and release movement following stroke and how that capacity is affected by the complexity of the task being performed. Such a tool may be useful for assessing functional impairments and tracking changes during the rehabilitation process.

Deep Learning: Predicting Environments From Short-Time Observations of Postural Balance.

OBJECTIVE: This study introduces a deep learning approach to accurately predict challenging mechanical environments that possibly cause decreasing postural stability. METHODS: Dual-axis robotic platforms were utilized to simulate various environments and collect center-of-pressure data during narrow and wide stance. A convolutional neural network (CNN) was developed to predict environmental conditions given segmented time-series balance data. Different window sizes were examined to investigate its minimal length for reliable prediction. Effectiveness of the presented CNN was additionally compared with that of conventional machine learning models. Its applicability with low sampled data or more natural stance data was then evaluated. RESULTS: The CNN achieved above 94.5% in the overall prediction accuracy even with 2.5-second length postural sway data, which cannot be achieved by traditional machine learning (ps < 0.05). Increasing data length beyond 2.5 seconds slightly improved the accuracy of CNN but substantially increased training time (60% longer). Importantly, results from averaged normalized confusion matrices revealed that CNN is much more capable of differentiating the mid-level environmental condition. Deep learning could also produce comparable performance in predicting environments even with much lower sampled data or with standing posture changed. CONCLUSION: CNN removed the burden of feature preparation and accurately predicted environments when dealing with short-length data. It also indicated potentials to real life applications. SIGNIFICANCE: This study contributes to the advancement of wearable devices and human interactive robots (e.g., exoskeletons and prostheses) by predicting environmental contexts and preventing potential falls.

Multi-degrees-of-freedom soft robotic ankle-foot orthosis for gait assistance and variable ankle support.

This paper presents the design, modeling, analysis, fabrication, and experimental characterization of the Soft Robotic Ankle-Foot Orthosis (SR-AFO), which is a wearable soft robot designed for ankle assistance, and a pilot human study of its use. Using two novel pneumatically-powered soft actuators, the SR-AFO is designed to assist the ankle in multiple degrees-of-freedom during standing and walking tasks. The flat fabric pneumatic artificial muscle (ff-PAM) contracts upon pressurization and assists ankle plantarflexion in the sagittal plane. The Multi-material Actuator for Variable Stiffness (MAVS) aids in supporting ankle inversion/eversion in the frontal plane. Analytical models of the ff-PAM and MAVS were created to understand how the changing of the design parameters affects tensile force generation and stiffness support, respectively. The models were validated by both finite element analysis and experimental characterization using a universal testing machine. A set of human experiments was performed with able-bodied participants to evaluate: 1) lateral ankle support during quiet standing, 2) lateral ankle support during walking over compliant surfaces, and 3) plantarflexion assistance during push-off in treadmill walking. Group results revealed increased lateral ankle stiffness during quiet standing with the MAVS active, reduced lateral ankle deflection while walking over compliant surfaces with the MAVS active, and reduced muscle effort in ankle platarflexors during 40-60% of the gait cycle with the dual ff-PAM active. The SR-AFO shows promising results in providing lateral ankle support and plantarflexion assistance with able-bodied participants, which suggests a potential to help restore the gait of impaired users in future trials.

The assessment of center of mass and center of pressure during quiet stance: Current applications and future directions.

This perspective article provides a brief review of our understanding of how center of pressure (CoP) and center of mass (CoM) are traditionally utilized to measure quiet standing and how technological advancements are allowing for measurements to be derived outside the confines of a laboratory setting. Furthermore, this viewpoint provides descriptions of what CoP and CoM outcomes may reflect, a discussion of recent developments in selected balance outcomes, the importance of measuring instantaneous balance outcomes, and directions for future questions/research. Considering the enormous number and cost of falls annually, conclusions drawn from this perspective underscore the need for more cohesive efforts to advance our understanding of balance performance. As we refine the technology and algorithms used to portably assess postural stability, the question of which measurement (i.e. CoP or CoM) to utilize seems to be highly dependent on the question being asked. Further, the complexity of the question appears to span multiple disciplines and cultivate exploration of the intrinsic mechanisms of stability. Recently developed multi-dimensional methods for assessing balance performance may provide additional insight into balance, improving our ability to predict balance impairments and falls outside the laboratory and in the clinic. However, additional work will be necessary to understand the clinical significance and predictive capacity of these outcomes in various fall-prone populations.

Optimizing the Rigid or Compliant Behavior of a Novel Parallel-Actuated Architecture for Exoskeleton Robot Applications.

The purpose of this work is to optimize the rigid or compliant behavior of a new type of parallel-actuated robot architecture developed for exoskeleton robot applications. This is done in an effort to provide those that utilize the architecture with the means to maximize, minimize, or simply adjust its stiffness property so as to optimize it for particular tasks, such as augmented lifting or impact absorption. This research even provides the means to produce non-homogeneous stiffness properties for applications that may require non-homogeneous dynamic behavior. In this work, the new architecture is demonstrated in the form of a shoulder exoskeleton. An analytical stiffness model for the shoulder exoskeleton is created and validated experimentally. The model is then used, along with a method of bounded nonlinear multi-objective optimization to configure the parallel substructures for desired rigidity, compliance or nonhomogeneous stiffness behavior. The stiffness model and its optimization can be applied beyond the shoulder to any embodiment of the new parallel architecture, including hip, wrist and ankle robot applications. In order to exemplify this, we present the rigidity optimization for a theoretical hip exoskeleton.

Quantification and Modeling of Ankle Stiffness During Standing Balance.

OBJECTIVE: This study investigates the factors contributing to the modulation of ankle stiffness during standing balance and evaluates the reliability of linear stiffness models. METHODS: A dual-axis robotic platform and a visual feedback display were used to quantify ankle stiffness in both the sagittal and frontal planes while subjects controlled different levels of ankle muscle co-contraction, center-of-pressure (CoP), and loading on the ankle. RESULTS: Results of 40 subjects demonstrated that ankle stiffness in the sagittal plane linearly increased with the increasing level of these three factors. The linear model relating the change in these factors from the baseline measurements during quiet standing to the change in weight normalized ankle stiffness resulted in high reliability (R2 = 0.83). Ankle stiffness in the frontal plane increased with the increasing ankle muscle co-contraction and ankle loading, but the linearity was less obvious. It also exhibited a clear nonlinear trend when CoP was shifted mediolaterally. Consequently, the reliability of the linear model was low for ankle stiffness in the frontal plane (R2 = 0.37). CONCLUSION: During standing balance, ankle stiffness in the sagittal plane could be well explained by a linear model if ankle muscle activation, CoP, and ankle loading were collectively considered. However, the linear model cannot capture highly variable and nonlinear ankle stiffness characteristics in the frontal plane. SIGNIFICANCE: The outcomes of this study could benefit the development of lower-extremity robots and their controllers. Furthermore, the ankle stiffness models could be used as a baseline in developing patient-specific ankle rehabilitation protocols.

Sex Differences in Human Ankle Stiffness During Standing Balance.

The purpose of this study is to quantify sex differences in 2-dimensional (2D) ankle stiffness during upright standing balance and investigate the mechanisms for the differences. A dual-axis robotic platform, capable of perturbing the ankle and measuring the corresponding ankle torques in both the sagittal and frontal planes, was used to reliably quantify the 2D ankle stiffness while healthy young human subjects perform a range of standing balance tasks, specifically, ankle muscle co-contraction tasks, weight-bearing tasks, and ankle torque generation tasks. In all task conditions and in both planes of ankle motion, ankle stiffness in males was consistently greater than that in females. Among all 26 experimental conditions, all but 2 conditions in the frontal plane showed statistically significant sex differences. Further investigation on the normalized ankle stiffness, scaled by weight times height, suggests that while sex differences in ankle stiffness in the sagittal plane could be explained by sex differences in anthropometric factors as well as neuromuscular factors, the differences in the frontal plane are mostly explained by anthropometric factors. This study also demonstrates that the sex differences in the sagittal plane were significantly higher as compared to those in the frontal plane. The results in this study will provide a foundation for not only characterizing sex differences in ankle stiffness during locomotion, but also investigating sex differences in lower body stability and risk of ankle injury.