Human gait analysis with wearable sensors has been widely used in various applications, such as daily life healthcare, rehabilitation, physical therapy, and clinical diagnostics and monitoring. In particular, ground reaction force (GRF) provides critical information about how the body interacts with the ground during locomotion. Although instrumented treadmills have been widely used as the gold standard for measuring GRF during walking, their lack of portability and high cost make them impractical for many applications. As an alternative, low-cost, portable, wearable insole sensors have been utilized to measure GRF; however, these sensors are susceptible to noise and disturbance and are less accurate than treadmill measurements. Deep learning has shown potential in addressing these issues, but such methods are computationally expensive and often require extensive computing resources, limiting their feasibility for real-time and portable systems. To address these challenges, we propose a Time-aware Knowledge Distillation framework for GRF estimation from insole sensor data. This framework leverages similarity and temporal features within a mini-batch during the knowledge distillation process, effectively capturing the complementary relationships between features and the sequential properties of the target and input data. The performance of the lightweight models distilled through this framework was evaluated by comparing GRF estimations from insole sensor data against measurements from an instrumented treadmill. Various teacher-student model architectures and learning strategies were evaluated across multiple performance metrics using data collected at different walking speeds. Empirical results demonstrated that Time-aware Knowledge Distillation outperforms current baselines in GRF estimation from wearable sensor data. Moreover, our method significantly reduces the number of training parameters needed for GRF estimation, offering a data- and resource-efficient solution for human gait analysis while achieving excellent accuracy and model reliability.

This study presents the design and validation of a variable stiffness actuator incorporating multiple cam mechanisms. The actuator is intended for use in walking assistance, focusing on assisting individuals with diminished ankle function. This study highlights the advantages of variable stiffness actuators over traditional and other modern actuators in mobility assistance. The working principles of the proposed Variable Stiffness Spiral Cam Actuator (VS-SCA) are described, focusing on the cantilever beams with adjustable supports, main cam mechanism, and symmetric support positioning architecture utilizing an Archimedean spiral cam. The design and fabrication process are discussed, considering system design considerations, cantilever beam design, cam design, and spiral cam design. The analytical methodology used for validation is also presented, which connects the subsystems of the actuator and allows for the determination of effective torsional stiffness. The experimental validation showed that the VS-SCA provides a range of stiffness from 20 to 75 Nm/rad for dorsiflexion, necessary for providing ankle assistance during the push-off phase of walking, while maintaining low stiffness (4 - 12 Nm/rad) for plantarflexion not to hinder natural ankle motion in the swing phase.

This study validates the effectiveness of a recently developed parallel-actuated shoulder exoskeleton robot for the purpose of characterizing the neuromuscular properties of the human shoulder joint. In particular, shoulder mechanical impedance was quantified, which can be represented by a 2ndorder system consisting of spring, damper and inertia. The shoulder exoskeleton robot, which utilizes a new type of 4-bar spherical parallel manipulator (4B-SPM), has inherently low inertia and as a result can provide fast perturbations that are often essential for characterizing neuromuscular properties. The robot was first evaluated by using a physical shoulder mockup with adjustable and known spring and mass properties. The results of the mockup test confirmed the reliability of the robot for the characterization of the mockup properties. Stiffness of the tested springs was accurately quantified with an error of less than 1.6 Nm/rad in any of the tested conditions. A pilot study with 5 human subjects further confirmed that the robot could be successfully used to quantify multi-dimensional human shoulder impedance in both pitch and yaw directions with high reliability (R2> 0.97). The average human shoulder stiffness and damping at around the neutral arm posture under low muscle activation (< 5% maximum voluntary contraction) were 30.9 Nm/rad and 3.0 Nms/rad, respectively.

This paper presents an experimental study that investigated how humans interact with viscous, damping-defined mechanical environments and quantified the lower bounds of robotic damping that they can stably interact with. Human subjects performed posture maintenance tasks for different arm postures while holding a robotic arm manipulator simulating unstable (negative) damping-defined environments and applying rapid perturbations to disturb the arm posture and challenge arm stability. The results of this study demonstrated that the lower bound of robotic damping for stable physical human-robot interaction was more than twice as low in the anterior-posterior (AP) direction than the medial-lateral (ML) direction, with lower limits of -50.3 Ns/m and -21.6 Ns/m in the AP and ML directions, respectively. The results further showed that the human arm is less capable of adjusting to the unstable environments when it is close to the body and laterally displaced for the AP and ML directions, respectively. Secondary analysis on the kinematic response in the phase space also demonstrated that arm stability in the unstable environments can be more easily achieved in the AP than ML direction. The outcomes of this study can be used to design less conservative robotic impedance or admittance controllers that utilize a wider range of robotic damping up to a certain extent of negative damping but do not compromise coupled stability of the human-robot system, which could improve the overall performance in physical human-robot interaction by achieving more agile operations and reducing user effort.

This paper presents the design, fabrication, and preliminary results of a soft hip exosuit to assist hip flexion and extension during walking. The exosuit uses soft and compliant materials to create a wearable robot that has a low profile, low mass, and is highly flexible to freely move with the user’s natural range of motion. The Soft Robotic Hip Exosuit (SR-HExo) consists of flat fabric pneumatic artificial muscles (ff-PAM) that contract when pressurized. The ff-PAM actuators are oriented in an ‘X’ shape to allow for natural range of motion across the hip joint and can generate 190 N of force at 200 kPa in a 0.3 sec window. The ‘X’ configuration (X-ff-PAM) actuators were placed on the anterior and posterior sides of the hip joint with height adjustable Velcro straps. Extension assistance and flexion assistance was provided in 10-45% and 50-90% of the gait cycle, respectively. To evaluate the effectiveness of the SR-HExo with healthy participants, hip range of motion and muscle activity during walking were monitored using a motion capture system and surface electromyography sensors. The impact of the SR-HExo on the range of motion was minimal with only a 4.0oreduction from the target range of motion of 30o. The exosuit contributed to reducing hip muscle activity. Hip extensor muscles showed a reduction of 13.1% for the gluteus maximus and 6.6% for the biceps femoris. Hip flexor muscles showed a reduction of 10.7% for the iliacus and 27.7% for the rectus femoris.

This article presents a multi-degree-of-freedom variable damping controller to manage the trade-off between stability and agility and to reduce user effort in physical human-robot interaction. The controller accounts for the human body's inherent impedance properties and applies a range of robotic damping from negative (energy injection) to positive (energy dissipation) values based on the user's intent of motion. To evaluate the effectiveness of the proposed controller in balancing the trade-off between stability/agility and reducing user effort, two studies are performed on both the human upper-extremity and lower-extremity to represent both industrial and rehabilitation applications of the proposed controller. These studies required subjects to perform a series of multidimensional target reaching tasks while the human user interacted with either the end-effector of a robotic arm for the upper-extremity study or a wearable ankle robot for the lower-extremity study. Stability, agility, and user effort are quantified by a variety of performance metrics. Stability is quantified by both overshoot and stabilization time. Mean and maximum speed are used to quantify agility. To quantify the user effort, both overall and maximum muscle activation, and mean and maximum root-mean-squared interaction force are calculated. The results of both the upper- and lower-extremity studies demonstrate that the controller is able to reduce user effort while increasing agility at a negligible cost to stability.

This paper presents a variable damping controller to improve the trade-off between agility and stability in physical human-robot interaction (pHRI), while reducing user effort. Variable robotic damping, defined as a dual-sided logistic function, was determined in real time throughout a range of negative to positive values based on the user's intent of movement. To evaluate the effectiveness of the proposed controller, we performed a set of human experiments with subjects interacting with the end-effector of a 7 degree-of-freedom robot. Twelve subjects completed target reaching tasks under three robotic damping conditions: fixed positive, fixed negative, and variable damping. On average, the variable damping controller significantly shortened the rise time by 22.4% compared to the fixed positive damping. It is also important to note that the rise time in the variable damping condition was as fast as that in the fixed negative damping condition and there was no statistical difference between the two conditions. The variable damping controller significantly decreased the percentage overshoot by 49.6% and shortened the settling time by 29.0% compared to the fixed negative damping. Both the maximum and mean root-mean-squared (RMS) interaction forces were significantly lower in the variable damping condition than the other two fixed damping conditions, i.e., the variable damping controller reduced user effort. The maximum and mean RMS interaction forces were at least 17.3% and 20.3% lower than any of the fixed damping conditions, respectively. The results of this study demonstrate that humans can extract the benefits of the variable damping controller in the context of pHRI, as it significantly improves the trade-off between agility and stability and reduces user effort in comparison to fixed damping controllers.

This paper presents a soft robotic ankle-foot orthosis (SR-AFO) exosuit designed to provide support to the human ankle in the frontal plane without restricting natural motion in the sagittal plane. The SR-AFO exosuit incorporates inflatable fabric-based actuators with a hollow cylinder design which requires less volume than the commonly used solid cylinder design for the same deflection. The actuators were modeled and characterized using finite element analysis techniques and experimentally validated. The SR-AFO exosuit was evaluated on healthy participants in both a sitting position using a wearable ankle robot and a standing position using a dual-axis robotic platform to characterize the effect of the exosuit on the change of 2D ankle stiffness in the sagittal and frontal planes. For both sitting and standing test protocols, a trend of increasing ankle stiffness in the frontal plane was observed up to 50 kPa while stiffness in the sagittal plane remained relatively constant over pressure levels. During quiet standing, the exosuit could effectively change eversion stiffness at the ankle joint from about 20 to 70 Nm/rad at relatively low- pressure levels (<; 30 kPa). Eversion stiffness was 84.9 Nm/rad at 50 kPa, an increase of 387.5% from the original free foot stiffness.

This paper presents the design of the Multi-material Actuator for Variable Stiffness (MAVS), which consists of an inflatable soft fabric actuator fixed between two layers of rigid retainer pieces. The MAVS is designed to be integrated with a soft robotic ankle-foot orthosis (SR-AFO) exosuit to aid in supporting the human ankle in the inversion/eversion directions. This design aims to assist individuals affected with chronic ankle instability (CAI) or other impairments to the ankle joint. The MAVS design is made from compliant fabric materials, layered and constrained by thin rigid retainers to prevent volume increase during actuation. The design was optimized to provide the greatest stiffness and least deflection for a beam positioned as a cantilever with a point load. Geometric programming of materials was used to maximize stiffness when inflated and minimize stiffness when passive. An analytic model of the MAVS was created to evaluate the effects in stiffness observed by varying the ratio in length between the rigid pieces and the soft actuator. A finite element analysis (FEA) was generated to analyze and predict the behavior of the MAVS prior to fabrication. The results from the analytic model and FEA study were compared to experimentally obtained results of the MAVS. The MAVS with the greatest stiffness was observed when the gap between the rigid retainers was smallest and the rigid retainer length was smallest. The MAVS design with the highest stiffness at 100 kPa was determined, which required 26.71 ± 0.06 N to deflect the actuator 20 mm, and a resulting stiffness of 1,335.5 N/m and 9.1% margin of error from the model predictions.

This paper presents an experimental study to investigate how humans interact with a robotic arm simulating primarily unstable, damping-defined, mechanical environments, and to quantify lower bounds of robotic damping that humans can stably interact with. Human subjects performed posture maintenance tasks while a robotic arm simulated a range of negative damping-defined environments and transiently perturbed the human arm to challenge postural stability. Analysis of 2-dimensional kinematic responses in both the time domain and phase space allowed us to evaluate stability of the coupled human-robot system in both anterior-posterior (AP) and medial-lateral (ML) directions, and to determine the lower bounds of robotic damping for stable physical human-robot interaction (pHRI). All subjects demonstrated higher capacity to stabilize their arm against negative damping-defined environments in the AP direction than the ML direction, evidenced by all 3 stability measures used in this study. Further, the lower bound of robotic damping for stable pHRI was more than 3.5 times lower in the AP direction than the ML direction: -30.0 Ns/m and -8.2 Ns/m in the AP and ML directions, respectively. Sensitivity analysis confirmed that the results in this study were relatively insensitive to varying experimental conditions. Outcomes of this study would allow us to design a less conservative robotic impedance controller that utilizes a wide range of robotic damping, including negative damping, and achieves more transparent and agile operations without compromising coupled stability and safety of the human-robot system, and thus improves the overall performance of pHRI.

This paper presents a variable damping control strategy to improve trade-off between agility/performance and stability in the control of the ankle exoskeleton robot. Depending on the user's intent of movement, the proposed variable damping controller determines the robotic ankle damping from negative to positive damping values. The range of damping values is determined by incorporating the knowledge of human ankle damping in order to always secure stability of the ankle joint of the coupled human-robot system. To evaluate the effectiveness of the proposed controller, we performed a set of human experiments with three different robotic damping conditions: fixed positive damping, fixed negative damping, and variable damping. Comparison of the two fixed damping conditions confirmed that there exists a clear trade-off between ankle agility and stability. Further, analysis of the variable damping condition demonstrated that humans could get benefits of not only positive damping to stabilize the ankle but also negative damping to enhance the agility of ankle movement as necessary during dynamic ankle movement. On average, the variable damping condition improved the agility of ankle movement by 76% and stability by 37% compared to the constant positive damping condition and the constant negative damping condition, respectively. Outcomes of this study would allow us to design a robotic controller that significantly improves agility/performance of the human-robot system without compromising its coupled stability.

The purpose of this work is to introduce a newly developed exoskeleton robot designed to characterize the neuromuscular properties of the shoulder, including intrinsic and reflexive mechanisms, during static posture and dynamic movement in a 3-dimensional space. Quantitative characterization of these properties requires fast perturbation (>100°/s) to separate their contribution from that of voluntary mechanism. Understanding these properties of the shoulder control could assist in the rehabilitation or enhancement of upper limb performance during physical human-robot interaction. The device can be described as a new type of spherical parallel manipulator (SPM) that utilizes three 4-bar (4B) substructures to decouple and control roll, pitch and yaw of the shoulder. By utilizing a parallel architecture, the 4BSPM exoskeleton has the advantage of high acceleration, fast enough to satisfy the speed requirement for the characterization of distinct neuromuscular properties of the shoulder. In this work, the prototype is presented, along with an evaluation of its position accuracy and step response. The development and preliminary testing of the 4B-SPM exoskeleton presented in this work demonstrates its potential to be a useful tool for studying the neuromuscular mechanisms of the shoulder joint.

The purpose of this work is to optimize the stiffness of a novel parallel-actuated robotic exoskeleton designed to offer a large workspace. This is done in an effort to help provide a solution to the issue wearable parallel actuated robots face regarding a tradeoff between stiffness and workspace. Presented in the form of a shoulder exoskeleton, the device demonstrates a new parallel architecture that can be used for wearable hip, ankle and wrist robots as well. The stiffness of the architecture is dependent on the placement of its actuated substructures. Therefore, it is desirable to place these substructures effectively so as to maximize dynamic performance for any application. In this work, an analytical stiffness model of the device is created and validated experimentally. The model is then used, along with a method of bounded nonlinear multi-objective optimization to configure the parallel actuators so as to maximize stiffness for the entire workspace. Furthermore, it is shown how to use the same technique to optimize the device for a particular task, such as lifting in the sagittal plane.

This paper presents quantification of multidimensional ankle stability in relation to mechanical environments having different levels of stability. This study, for the first time, explores the range of stiffness-defined haptic environments over which young healthy individuals can maintain stability despite aggressive perturbation. Ankle stability was quantified in 2 degree-of-freedom (DOF) of the ankle, in both the sagittal and frontal planes. Importantly, the magnitude of negative environmental stiffness that the subjects could maintain stability is 4 times as great in the sagittal plane as in the frontal plane. In addition to managing a wider range of unstable environments in the sagittal plane, subjects were also more efficient at regaining stability after perturbation and less sensitive to changes in the environmental stiffness. Outcomes of this study would be beneficial to the design and control of robots physically interacting with human lower extremities, such as lower-limb exoskeletons and powered ankle-foot orthoses.

This paper presents a novel multi-axis robotic platform for the characterization of two important neuromuscular properties of the human ankle: mechanical impedance and reflex responses. The platform is capable of producing highly accurate position perturbations up to an angular speed of 200°/s and emulating a wide range of haptic environments in two degree-of-freedom (DOF) of the ankle: dorsiflexion-plantarflexion (in the sagittal plane) and inversion-eversion (in the frontal plane). This unique feature allows us to seamlessly simulate realistic mechanical environments and to transiently perturb the ankle for the characterization of its neuromuscular properties. The position controller achieved the accuracy of 0.05° even under the loading condition (a subject of 95 kg standing on the platform). The haptic controller could successfully emulate a wide range of mechanical environments, from compliant to rigid (50-1000 Nm/rad), with an error of 2% of the commanded values. We further validated that the proposed platform could reliably estimate the stiffness of a mockup (17.8-171.0 Nm/rad) that resembles the human ankle within an error of 1.6%. Finally we demonstrated that the platform could be successfully utilized to elicit medium-latency and long-latency reflex responses of the ankle muscles. Implications for future ankle studies are discussed.

Managing the trade-off between performance and stability is a crucial issue in physical human-robot interaction, and this has become more important than ever apace with growing needs for physically human-interactive robots in many fields, such as assistive robotics and rehabilitation robotics. In this paper, we present two essential considerations for design and control of robots physically interacting with humans: energetic passivity and mechanical impedance. Characterization of passive, dissipative, and active behavior of the human neuromuscular system is essential to ensure and control coupled stability in physical human-robot interaction. In addition, characterization of human mechanical impedance at the interaction port provides detailed quantitative information to describe interactive dynamics. The importance of these characterizations is demonstrated by simple examples and the authors' previous research on the human ankle. Implications for quantitative guidelines for robot design and control are discussed.

This paper presents a new method to characterize human ankle mechanical impedance during treadmill locomotion with a wearable ankle robot, Anklebot. An ensemble-based system identification method was used to investigate the time-varying behavior of ankle mechanical impedance in two degrees of freedom, both in the sagittal and frontal planes. We also provide solutions to overcome the limitations of original ensemble-based methods in practical applications. A pilot study of three human subjects demonstrated the efficacy of our approach. Analysis results showed clear time-varying behaviors of ankle impedance across the gait cycle except in the mid- and terminal-stance phases, and these behaviors were accurately approximated as a second-order model with stiffness, damping, and inertia components. Interestingly, all three subjects showed similar time-varying behaviors in both degrees of freedom: impedance increased around heel-strike and decreased significantly at the end of the stance phase.

This paper describes a new learning by observation algorithm based on Bayesian networks and game pattern graphs. Even with minimal knowledge of a game or human instructions, the robot can learn the game rules by watching human demonstrators repeatedly play the game multiple times. Based on the knowledge acquired from this learning process, represented in Bayesian networks and game pattern graphs, the robot can play games as robustly as humans do. Our learning algorithm for human-robot game interaction is implemented using a teddy bear-like robot and is demonstrated by application to well-known social games, specifically Rock-Paper-Scissors, Muk-Chi-ba and Blackjack.