Human-In-The-Loop Optimization of A Wearable Robot Using Foot Pressure Sensors

Background: Individuals with below-knee amputation (BKA) experience increased physical effort when walking, and the use of a robotic ankle-foot prosthesis (AFP) can reduce such effort. Our prior study on a robotic AFP showed that walking effort could be reduced if the robot is personalized to the wearer. The personalization is accomplished using human-in-the-loop (HIL) optimization, in which the cost function is based on a real-time physiological signal indicating physical effort. The conventional physiological measurement, however, requires a long estimation time, hampering real-time optimization due to the limited experimental time budget. In addition, the physiological sensor, based on respiration uses a mask with rigid elements that may be difficult for the wearer to use. Prior studies suggest that a symmetry measure using a less intrusive sensor, namely foot pressure, could serve as a metric of gait performance. This study hypothesized that a function of foot pressure, the symmetric foot force-time integral, could be used as a cost function to rapidly estimate the physical effort of walking; therefore, it can be used to personalize assistance provided by a robotic ankle in a HIL optimization scheme. Methods: We developed a new cost function derived from a well-known clinical measure, the symmetry index, by hypothesizing that foot force-time integral (FFTI) symmetry would be highly correlated with metabolic cost. We conducted experiments on human participants (N = 8) with simulated amputation to test the new cost function. The study consisted of a discrete trial day, an HIL optimization training day, and an HIL optimization data collection day. We used the discrete trial day to evaluate the correlation between metabolic cost and a cost function using symmetric FFTI percentage. During walking, we varied the prosthetic ankle stiffness while measuring foot pressure and metabolic rate. On the second and third days, HIL optimization was used to find the optimal stiffness parameter with the new cost function using symmetric FFTI percentage. Once the optimal stiffness parameter was found, we validated the performance with comparison to a weight-based stiffness and control-off conditions. We measured symmetric FFTI percentage during the stance phase, prosthesis push-off work, metabolic cost, and user comfort in each condition. We expected the optimized prosthetic ankle stiffness based on the newly developed cost function could reduce the energy expenditure during walking for the individuals with simulated amputation. Results: We found that the cost function using symmetric foot force-time integral percentage presents a reasonable correlation with measured metabolic cost (Pearson’s R > 0.62). When we employed the new cost function in HIL ankle-foot prosthesis parameter optimization, 8 individuals with simulated amputation reduced their cost of walking by 15.9% (p = 0.01) and 16.1% (p = 0.02) compared to the weight-based and control-off conditions, respectively. The symmetric FFTI percentage for the optimal condition tended to be closer to the ideal symmetry value (50%) compared to weight-based (p = 0.23) and control-off conditions (p = 0.04). Conclusion: This study suggests that foot force-time integral symmetry using foot pressure sensors can be used as a cost function when optimizing a wearable robot parameter.

Technology Anxiety on Attitude Towards Technology: The Moderating Effect of Age-friendly Environment

The purpose of this study was to examine the moderating effect of an age-friendly environment on the relationship between technology anxiety and attitude towards technology among Korean older adults. We collected data by online surveys in February 2021, and the sample was 324 Korean older adults aged 65 and above. The dependent variable was the attitude towards technology, which meant the appraisal about using a wearable robot for exercise. The independent variable was technology anxiety, meaning an individual’s apprehension of using a wearable robot. The moderating variable was age-friendly environment, which comprises domains of the physical environment, social environment, and municipal services. The higher the score is, the more age-friendly the environment was perceived. Control variables were age, sex, education, household income. The moderation effect was estimated by bootstrapping and PROCESS macro. Results showed that when older adults showed a higher level of technology anxiety, their attitude towards technology was less positive. Moreover, the moderation effect of an age-friendly environment was significant. Concretely, in the case of living in a less age-friendly environment, older adults with a higher level of technology anxiety were more likely to report a less positive attitude towards technology. However, the effect of technology anxiety on attitude towards technology was not significant among older adults living in a more age-friendly environment. It suggested that a practical intervention to reduce the level of technology anxiety is in need in order to promote a positive attitude towards technology, especially for older adults living in a less age-friendly environment.

Understanding Technology Anxiety by the Interaction between Social Support and Educational Context

The purpose of this study was to examine the interaction effects of social support from family and educational contexts on technology anxiety among Korean older adults. We collected data by online recruiting in February 2021, and the sample was Korean older adults without dementia (N=310; 65-89 years old). The dependent variable was technology anxiety, which meant the expected degree of worry under the assumption that a wearable robot for exercise was used. Independent variables were four types of social support (emotional, instrumental, physical, and financial support) provided by family members, such as spouse, children, or siblings. The moderating variable was the binary educational context (high school and under=0; college level and over=1). Interaction effects were estimated by bootstrapping and PROCESS macro with four regression models about each type of social support. Results showed the interaction effect between physical support and educational context was significant on technology anxiety. Concretely, getting more physical support was significantly associated with a lower level of technology anxiety for highly educated older adults, while it was not significant for less-educated older adults. There was no additional type of social support which had not only significant interaction effects with educational context but also main effects on technology anxiety. It suggested that providing direct physical help, including daily care or assistance, could decrease feeling technology anxiety, especially not for less-educated seniors but for highly educated Korean older adults.

Consideration and analysis for attachment position of supernumerary robotic limbs

The supernumerary robotic limbs(SRLs) is a new type of wearable robot that assists the operator with additional robotic limbs and allows the operator to perform multiple tasks simultaneously. Due to the SRLs having various combinations of robotic limb and attachment positions, and there is an insufficient discussion on the influence of different wear positions on the SRLs. Therefore, this paper improved the evaluation indexes from previous studies and presents an experimental evaluation of the performance of indexes between humans and SRLs. This paper analyzed the 5 different positions based on the improved evaluation indexes, 2 optimal positions are found with the simulation experiment. Then the two design factors to improve the performance of evaluation indexes are discussed. The evaluation indexes can be utilized as a design parameter for evaluating human-robot interactions of SRLs.

Assessment of a passive wearable robot for reducing low back disorders during rebar work

Low back disorder continues to be prevalent amongst construction workers, especially the rebar workers who are often engaged in repetitive stooping postures. Wearable robots, exoskeletons, are recent ergonomic interventions currently explored in the construction industry that have potentials of reducing the risks of low back pain by augmenting users’ body parts and reducing demands on the back. This paper presents the assessment of a commercially available passive wearable robot, BackX, designed for reducing low back disorder amongst rebar workers. The study evaluated the exoskeleton in terms of task performance and physiological conditions. Outcome measures such as completion time were employed to evaluate the effect of the exoskeleton on task performance, while activations of Erector Spinae and Latissimus Dorsi muscles, and perceived discomfort across body parts were employed to assess the physiological effects of the exoskeleton. The results indicated mixed effects of the exoskeleton on muscle activations. Although the results revealed that the exoskeleton can reduce muscle activations across the Latissimus Dorsi, mixed effects were observed for the Erector Spinae especially during the forward bending tasks. The exoskeleton reduced completion time by 50% during the rebar tasks. There was also a 100% reduction in perceived discomfort on the back, but discomfort was tripled at the chest region when the exoskeleton was worn. This study reveals the potentials of the exoskeleton for reducing low back disorder and improving productivity amongst the rebar workers. However, the unintended consequences such as increased discomfort at the chest region and activations of the muscles highlight the need for improving existing exoskeleton designs for construction work.

Learning to walk with a wearable robot in 880 simple steps: a pilot study on motor adaptation

Background Wearable robots have been shown to improve the efficiency of walking in diverse scenarios. However, it is unclear how much practice is needed to fully adapt to robotic assistance, and which neuromotor processes underly this adaptation. Familiarization strategies for novice users, robotic optimization techniques (e.g. human-in-the-loop), and meaningful comparative assessments depend on this understanding. Methods To better understand the process of motor adaptation to robotic assistance, we analyzed the energy expenditure, gait kinematics, stride times, and muscle activities of eight naïve unimpaired participants across three 20-min sessions of robot-assisted walking. Experimental outcomes were analyzed with linear mixed effect models and statistical parametric mapping techniques. Results Most of the participants’ kinematic and muscular adaptation occurred within the first minute of assisted walking. After ten minutes, or 880 steps, the energetic benefits of assistance were realized (an average of 5.1% (SD 2.4%) reduction in energy expenditure compared to unassisted walking). Motor adaptation was likely driven by the formation of an internal model for feedforward motor control as evidenced by the reduction of burst-like muscle activity at the cyclic end of robotic assistance and an increase in arm-swing asymmetry previously associated with increased cognitive load. Conclusion Humans appear to adapt to walking assistance from a wearable robot over 880 steps by forming an internal model for feedforward control. The observed adaptation to the wearable robot is well-described by existing three-stage models that start from a cognitive stage, continue with an associative stage, and end in autonomous task execution. Trial registration Not applicable.

Progress in the Triboelectric Human–Machine Interfaces (HMIs)-Moving from Smart Gloves to AI/Haptic Enabled HMI in the 5G/IoT Era

Entering the 5G and internet of things (IoT) era, human–machine interfaces (HMIs) capable of providing humans with more intuitive interaction with the digitalized world have experienced a flourishing development in the past few years. Although the advanced sensing techniques based on complementary metal-oxide-semiconductor (CMOS) or microelectromechanical system (MEMS) solutions, e.g., camera, microphone, inertial measurement unit (IMU), etc., and flexible solutions, e.g., stretchable conductor, optical fiber, etc., have been widely utilized as sensing components for wearable/non-wearable HMIs development, the relatively high-power consumption of these sensors remains a concern, especially for wearable/portable scenarios. Recent progress on triboelectric nanogenerator (TENG) self-powered sensors provides a new possibility for realizing low-power/self-sustainable HMIs by directly converting biomechanical energies into valuable sensory information. Leveraging the advantages of wide material choices and diversified structural design, TENGs have been successfully developed into various forms of HMIs, including glove, glasses, touchpad, exoskeleton, electronic skin, etc., for sundry applications, e.g., collaborative operation, personal healthcare, robot perception, smart home, etc. With the evolving artificial intelligence (AI) and haptic feedback technologies, more advanced HMIs could be realized towards intelligent and immersive human–machine interactions. Hence, in this review, we systematically introduce the current TENG HMIs in the aspects of different application scenarios, i.e., wearable, robot-related and smart home, and prospective future development enabled by the AI/haptic-feedback technology. Discussion on implementing self-sustainable/zero-power/passive HMIs in this 5G/IoT era and our perspectives are also provided.