Powered exoskeletons need actuators that are lightweight, compact, and efficient while allowing for accurate torque control. To satisfy these requirements, researchers have proposed using series elastic actuators (SEAs). SEAs use a spring in series with rotary or linear actuators. The spring compliance, in conjunction with an appropriate control scheme, improves torque control, efficiency, output impedance, and disturbance rejection. However, springs add weight to the actuator and complexity to the control, which may have negative effects on the performance of the powered exoskeleton. Therefore, there is an unmet need for new SEA designs that are lighter and more efficient than available systems, as well as for control strategies that push the performance of SEA-based exoskeletons without requiring complex modeling and tuning. This article presents the design, development, and testing of a novel SEA with high force density for powered exoskeletons, as well as the use of a two degree-of-freedom (2DOF) PID system to improve output impedance and disturbance rejection. Benchtop testing results show reduced output impedance and damping values when using a 2DOF PID controller as compared to a 1DOF PID controller. Human experiments with three able-bodied subjects (N = 3) show improved torque tracking with reduced root-mean-square error by 45.2% and reduced peak error by 49.8% when using a 2DOF PID controller. Furthermore, EMG data shows a reduction in peak EMG value when using the exoskeleton in assistive mode compared to the exoskeleton operating in transparent mode.

An active lifestyle can mitigate physical decline and cognitive impairment in older adults. Regular walking exercises for older individuals result in enhanced balance and reduced risk of falling. In this article, we present a study on gait monitoring for older adults during walking using an integrated system encompassing an assistive robot and wearable sensors. The system fuses data from the robot onboard Red Green Blue plus Depth (RGB-D) sensor with inertial and pressure sensors embedded in shoe insoles, and estimates spatiotemporal gait parameters and dynamic margin of stability in real-time. Data collected with 24 participants at a community center reveal associations between gait parameters, physical performance (evaluated with the Short Physical Performance Battery), and cognitive ability (measured with the Montreal Cognitive Assessment). The results validate the feasibility of using such a portable system in out-of-the-lab conditions and will be helpful for designing future technology-enhanced exercise interventions to improve balance, mobility, and strength and potentially reduce falls in older adults.

The biological ankle dorsiflexes several degrees during swing to provide adequate clearance between the foot and ground, but conventional energy storage and return (ESR) prosthetic feet remain in their neutral position, increasing the risk of toe scuffs and tripping. We present a new prosthetic ankle intended to reduce fall risk by dorsiflexing the ankle joint during swing, thereby increasing the minimum clearance between the foot and ground. Unlike previous approaches to providing swing dorsiflexion such as powered ankles or hydraulic systems with dissipative yielding in stance, our ankle device features a spring-loaded linkage that adopts a neutral angle during stance, allowing ESR, but adopts a dorsiflexed angle during swing. The ankle unit was designed, fabricated, and assessed in level ground walking trials on a unilateral transtibial prosthesis user to experimentally validate its stance and swing phase behaviors. The assessment consisted of three conditions: the ankle in an operational configuration, the ankle in a locked configuration (unable to dorsiflex), and the subject’s daily use ESR prosthesis. When the ankle was operational, minimum foot clearance (MFC) increased by 13 mm relative to the locked configuration and 15 mm relative to his daily use prosthesis. Stance phase energy return was not significantly impacted in the operational configuration. The increase in MFC provided by the passive dorsiflexing ankle prosthesis may be sufficient to decrease the rate of falls experienced by prosthesis users in the real world.

Back pain is one of the largest drivers of workplace injury and lost productivity in industries around the world. Back injuries were one of the leading reasons in resulting in days away from work at 38.5% across all occupations, increasing for manual laborers to 43%. While the cause of the back pain can vary across occupations, for materiel movers it is often caused from repetitive poor lifting. To reduce the issues, the Aerial Porter Exoskeleton (APEx) was created. The APEx uses a hip-mounted, powered exoskeleton attached to an adjustable vest. An onboard computer calculates the configuration of the user to determine when to activate. Lift form is assisted by using a novel lumbar brace mounted on the sides of the hips. Properly worn, the APEx holds the user upright while providing additional hip torque through a lift. This was tested by having participants complete a lifting test with the exoskeleton worn in the “on” configuration compared with the exoskeleton not worn. The APEx has been shown to deliver 30 Nm of torque in lab testing. The activity recognition algorithm has also been shown to be accurate in 95% of tested conditions. When worn by subjects, testing has shown average peak reductions of 14.9% BPM, 8% in VO2 consumption, and an 8% change in perceived effort favoring the APEx.

While active back-support exoskeletons can reduce mechanical loading of the spine, current designs include only one pair of actuated hip joints combined with a rigid structure between the pelvis and trunk attachments, restricting lumbar flexion and consequently intended lifting behavior. This study presents a novel active exoskeleton including actuated lumbar and hip joints as well as subject-specific exoskeleton control based on a real-time active low-back moment estimation. We evaluated the effect of exoskeleton support with different lumbar-to-hip (L/H) support ratios on spine loading, lumbar kinematics, and back muscle electromyography (EMG). Eight healthy males lifted 15 kg loads using three techniques without exoskeleton (NOEXO) and with exoskeleton: minimal impedance mode (MINIMP), L/H support ratio in line with a typical L/H net moment ratio (R0.8), lower (R0.5) and higher (R2.0) L/H support ratio than R0.8, and a mechanically fixed lumbar joint (LF; simulating hip joint-only exoskeleton designs).

EMG-driven musculoskeletal model results indicated that R0.8 and R0.5 yielded significant reductions in spinal loading (4–11%, p < .004) across techniques when compared to MINIMP, through reducing active moments (14–30%) while not affecting lumbar flexion and passive moments. R2.0 and LF significantly reduced spinal loading (8–17%, p < .001; 22–26%, p < .001, respectively), however significantly restricted lumbar flexion (3–18%, 24–27%, respectively) and the associated passive moments.

An L/H support ratio in line with a typical L/H net moment ratio reduces spinal loading, while allowing normal lifting behavior. High L/H support ratios (e.g., in hip joint-only exoskeleton designs) yield reductions in spinal loading, however, restrict lifting behavior, typically perceived as hindrance.

Continuous gait phase plays an important role in robotic prosthesis control. In this paper, we have conducted the offline adaptive estimation (at different speeds and on different ramps) of continuous gait phase of robotic transtibial prosthesis based on the adaptive oscillators. We have used the capacitive sensing method to record the deformation of the muscles. Two transtibial amputees joined in this study. Based on the strain signals of the prosthetic foot and the capacitive signals of the residual limb, the maximum and minimum of estimation errors are 0.80 rad and 0.054 rad, respectively, and their corresponding ratios in one gait cycle are 1.27% and 0.86%, respectively. This paper proposes an effective method to estimate the continuous gait phase based on the capacitive signals of the residual muscles, which provides a basis for the continuous control of robotic transtibial prosthesis.

Climbing stairs can become a daily obstacle for elderly people, and an exoskeleton can assist here. However, the exoskeletons that are designed to assist stair climbing are actuated in different ways. To find a minimal actuation configuration, we identify the assist phases by evaluating the power deficit of 11 healthy but weak elderly people (72.4 ± 2.1 years; 69–76 years; 1.67 ± 0.10 m; 74.88 ± 14.54 kg) compared to 13 younger people (24.0 ± 1.8 years; 22–28 years; 1.74 ± 0.10 m; 70.85 ± 11.91 kg) in a biomechanical study and discuss moment characteristics. Three-dimensional kinematics and ground reaction forces were collected, and kinematics, kinetics, and power characteristics of each subject for ascent and descent were calculated using inverse dynamics. Significant differences for power between both groups were assessed with statistical parametric mapping method using dynamic time warping. During ascent, the largest significant power deficit of the elderly subjects occurs in the single stance phase (SSP) during pull-up in the knee joint. During descent, significant mean power deficits of 0.2 and 0.8 W/kg for the highest deficit occur in the ankle joint in the beginning of the SSP and also in the knee joint in the same phase. Therefore, an exoskeleton should address the power deficit for knee extension (ascent: 1.0 ± 0.9 W/kg; descent: 0.3 ± 0.2 W/kg) and could assist the ankle during ascent and descent by an additional plantar flexion moment of 0.2 Nm/kg each.

The treatment and evacuation of people with lower limb fractures in austere environments presents unique challenges that assistive exoskeletal devices could address. In these dangerous situations, independent mobility for the injured can preserve their vital capabilities so that they can safely evacuate and minimize the need for additional personnel to help. This expert view article discusses how different exoskeleton archetypes could provide independent mobility while satisfying the requisite needs for portability, maintainability, durability, and adaptability to be available and useful within austere environments. The authors also discuss areas of development that would enable exoskeletons to operate more effectively in these scenarios as well as preserve the health of the injured limb so that definitive treatment after evacuation will produce better outcomes.

Passive exosuits have been vastly researched in the past decade for lifting tasks to alleviate the mechanical loading on the spine and reduce the lower back muscle activities in lifting tasks. Despite promising advantages of exosuits, factors such as comfort directly influence the user’s acceptability of such body-worn devices. Exosuits’ routing/anchoring points, which transmit device-to-body forces, remain the leading cause of discomfort among users. In the present study, we sought to investigate the effect of the routing element, that is, the “moment arm,” in altering the device-to-body forces and perceived discomfort. We first presented a simplified human–exosuit model to establish insight into the effect of the moment arm on the device-to-body forces acting at the shoulder (FS) and waist (FW). Further, an experimental investigation was conducted on 10 participants with six different exosuit moment arm configurations (C1, C2, C3, C4, C5, and C6) to investigate their effect on the device-to-body forces, perceived discomfort, and muscle activity using a passive back-assist exosuit emulator in a lifting/lowering task. Configuration C4 was found to be most beneficial in reducing device-to-body forces at the shoulder and waist by up to 44.6 and 22.2%, respectively, during lifting. Subjective scores also comprehended with the device-to-body forces, indicating that C4 produces significantly less discomfort for participants. The outcome of the study illustrates the importance of selecting an appropriate moment arm configuration for passive back support exosuits in alleviating the device-to-body forces and perceived discomfort.

The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot–ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher R-squared values. The average MAE score was less than 1.138° and 0.939° in sagittal and frontal planes, respectively, when training models for each speed and 2.15° and 1.14° when trained and evaluated for all speeds. These results indicate wearable smart socks to generalize foot–ankle kinematics over various walking speeds with relatively low error and could consequently be used to measure gait parameters without the need for a lab-constricted motion capture system.

The performative installation DeviceD utilizes a network of systems toward facilitating interaction between dancer, digital media, and audience. Central to the work is a wearable haptic feedback system able to wirelessly deliver vibrotactile stimuli, with the latter initiated by the audience through posting on Twitter social media platform; the system in use searches for specific mentions, hashtags, and keywords, with positive results causing the system to trigger patterns of haptic biofeedback across the wearable’s four actuator motors. The system acts as the intermediator between the audience’s online actions and the dancer receiving physical stimuli; the dancer interprets these biofeedback signals according to Laban’s Effort movement qualities, with the interpretation informing different states of habitual and conscious choreographic performance. In this article, the authors reflect on their collaborative process while developing DeviceD alongside a multidisciplinary team of technologists, detailing their experience of refining the technology and methodology behind the work while presenting it in three different settings. A literature review is used to situate the work among contemporary research on interaction over internet and haptics in performance practice; haptic feedback devices have been widely used within artistic work for the past 25 years, with more recent practice and research outputs suggesting an increased interest for haptics in the field of dance research. The authors detail both technological and performative elements making up the work, and provide a transparent evaluation of the system, as means of providing a foundation for further research on wearable haptic devices.

B-mode ultrasound (US) is often used to noninvasively measure skeletal muscle architecture, which contains human intent information. Extracted features from B-mode images can help improve closed-loop human–robotic interaction control when using rehabilitation/assistive devices. The traditional manual approach to inferring the muscle structural features from US images is laborious, time-consuming, and subjective among different investigators. This paper proposes a clustering-based detection method that can mimic a well-trained human expert in identifying fascicle and aponeurosis and, therefore, compute the pennation angle. The clustering-based architecture assumes that muscle fibers have tubular characteristics. It is robust for low-frequency image streams. We compared the proposed algorithm to two mature benchmark techniques: UltraTrack and ImageJ. The performance of the proposed approach showed higher accuracy in our dataset (frame frequency is 20 Hz), that is, similar to the human expert. The proposed method shows promising potential in automatic muscle fascicle orientation detection to facilitate implementations in biomechanics modeling, rehabilitation robot control design, and neuromuscular disease diagnosis with low-frequency data stream.

Recently, many kinds of shoulder-support exoskeletons have been developed and some of them are commercially available. However, to the best of our knowledge, shoulder-support exoskeletons that have neck-support mechanism have not been found. During the overhead work, physical strain is added to not only upper limb and shoulder but also neck of workers since the workers work keeping their face raised. Therefore, in this study, to reduce the physical strain on the neck during the overhead work, a movable headrest that can be attached to the shoulder assist device was developed, which has reclining and slide functions of a head. The main purpose of this article was to evaluate usefulness of the proposed movable headrest. To this end, measurements of electromyogram were carried out under simulating an overhead work activity, and the reduction effect for physical strain of the neck was compared among three types of headrests: (a) slide-type headrest which can slide the head backward and forward, (b) reclining-type headrest which can recline the head, and (c) reclining and slide-type headrest which can recline and slide the head. In addition, usefulness of the shoulder assist device with the proposed headrest was evaluated for a realistic overhead work activity through measurements of muscular stiffness of neck and shoulder. The experimental results showed that the existence of the headrest in the shoulder assist device is effective to reduce the physical strain to the workers, and that (c) reclining and slide-type headrest is the most effective among these three types of headrests.

Commonly, quantitative gait analysis post-stroke is performed in fully equipped laboratories housing costly technologies for quantitative evaluation of a patient’s movement capacity. Combining such technologies with an electromyography (EMG)-driven musculoskeletal model can estimate muscle force properties non-invasively, offering clinicians insights into motor impairment mechanisms. However, lab-constrained areas and time-demanding sensor setup and data processing limit the practicality of these technologies in routine clinical care. We presented wearable technology featuring a multi-channel EMG-sensorized garment and an automated muscle localization technique. This allows unsupervised computation of muscle-specific activations, combined with five inertial measurement units (IMUs) for assessing joint kinematics and kinetics during various walking speeds. Finally, the wearable system was combined with a person-specific EMG-driven musculoskeletal model (referred to as human digital twins), enabling the quantitative assessment of movement capacity at a muscle-tendon level. This human digital twin facilitates the estimation of ankle dorsi-plantar flexion torque resulting from individual muscle-tendon forces. Results demonstrate the wearable technology’s capability to extract joint kinematics and kinetics. When combined with EMG signals to drive a musculoskeletal model, it yields reasonable estimates of ankle dorsi-plantar flexion torques (R2 = 0.65 ± 0.21) across different walking speeds for post-stroke individuals. Notably, EMG signals revealing an individual’s control strategy compensate for inaccuracies in IMU-derived kinetics and kinematics when input into a musculoskeletal model. Our proposed wearable technology holds promise for estimating muscle kinetics and resulting joint torque in time-limited and space-constrained environments. It represents a crucial step toward translating human movement biomechanics outside of controlled lab environments for effective motor impairment monitoring.

Falls due to stumbles are a major cause of injury for many populations, and as such interventions to reduce fall risk have been a key focus of rehabilitation research. However, dedicated stumble recovery assistance in a powered lower-limb exoskeleton has yet to be explored as a fall mitigation intervention. Thus young, healthy adults ($ N=3 $) were recruited for a stumble recovery experiment to test the efficacy of knee exoskeleton stumble recovery assistance in improving an impaired stumble recovery response (i.e., the elevating strategy response). Leg weights were attached unilaterally to each participant’s shank to simulate walking and stumble recovery impairment, and a unilateral powered knee exoskeleton was worn on the same leg for walking and stumble recovery assistance. Ultimately, knee exoskeleton stumble recovery assistance served to improve participants’ elevating limb kinematics (i.e., increase thigh and knee motion) and reduce overall fall risk (i.e., reduce trunk motion and improve foot placement) during responses relative to their impaired response (i.e., with the leg weights and no assistance), and relative to their response while receiving only walking assistance. This initial exploration provides a first indication that knee exoskeleton stumble recovery assistance is a viable approach to improving an impaired stumble recovery response, which could serve two important use cases: (1) a safety mechanism for existing exoskeleton wearers, who may be less capable of recovering from stumbles due to the added weight or joint impedance of the device; (2) an external stumble recovery aid for fall-prone populations, such as the elderly or stroke survivors.

Multimodal digital data registered with wearable biosensors have emerged as highly complementary of clinical pencil-and-paper criteria, offering new insights in ways to detect and diagnose various aspects of Parkinson’s disease (PD). A pressing question is how to combine both the clinical knowledge of PD and the new technology to create interpretable digital biomarkers easily obtainable with off-the-shelf technology. Several challenges concerning disparity in biophysical units, anatomical differences across participants, sensor positioning, and sampling resolution are addressed in this work, along with identification of optimal parameters to automatically differentiate patients with PD from controls. We combine data from a multitude of biosensors registering signals from the central (electroencephalography) and peripheral (magnetometry, kinematics) nervous systems, inclusive of the autonomic nervous system (electrocardiogram), as the participants perform natural tasks requiring different levels of intentional planning and automatic control. We find that magnetometer data during walking, across a variety of amplitude and timing signals, provide optimal separation of PD from neurotypical controls. We conclude that using multimodal signals within the context of actions that bear different levels of intent, can be revealing of features of PD that would scape the naked eye. Further, we add that clinical criteria combined with such optimal digital parameter spaces offer a far more complete picture of PD than using either one of these pieces of data alone.

This paper proposes a novel origami-inspired adult diaper design that improves discretion by reducing sag and increasing wicking across the entire diaper pad. While other diapers rely on supporting elastics to reduce the sag of the diaper as a whole, this paper proposes an absorbent core that uses liquid activated shaping to take a specified shape. Origami-based folds are also incorporated into the diaper design to increase wicking performance. The paper introduces a disposable compliant mechanism waistband used to deploy the diaper, making it easier to put onto one’s body.

This work introduces a real-time intention decoding algorithm grounded in muscle synergies (Syn-ID). The algorithm detects the electromyographic (EMG) onset and infers the direction of the movement during reaching tasks to control a powered shoulder–elbow exoskeleton. Features related to muscle synergies are used in a Gaussian Mixture Model and probability accumulation-based logic to infer the user’s movement direction. The performance of the algorithm was verified by a feasibility study including eight healthy participants. The experiments comprised a transparent session, during which the exoskeleton did not provide any assistance, and an assistive session in which the Syn-ID strategy was employed. Participants were asked to reach eight targets equally spaced on a circumference of 25 cm radius (adjusted chance level: 18.1%). The results showed an average accuracy of 48.7% after 0.6 s from the EMG onset. Most of the confusion of the estimate was found along directions adjacent to the actual one (type 1 error: 33.4%). Effects of the assistance were observed in a statistically significant reduction in the activation of Posterior Deltoid and Triceps Brachii. The final positions of the movements during the assistive session were on average 1.42 cm far from the expected ones, both when the directions were estimated correctly and when type 1 errors occurred. Therefore, combining accurate estimates with type 1 errors, we computed a modified accuracy of 82.10±6.34%. Results were benchmarked with respect to a purely kinematics-based approach. The Syn-ID showed better performance in the first portion of the movement (0.14 s after EMG onset).