A Real-Time Lift Detection Strategy for a Hip Exoskeleton

Controlling Negative and Positive Power for Efficiency Enhancement and Muscle Strain Mitigation During Squatting with a Portable Knee Exoskeleton

PURPOSE

Workers face a notable risk of musculoskeletal injuries when performing squatting tasks. Knee exoskeletons offer a promising solution to mitigate muscle strain through squat assistance. However, existing studies on knee exoskeletons lack a comprehensive study that meets the multifaceted requirements of squatting assistance in terms of portability, efficiency, and muscle strain mitigation. Furthermore, another open research question pertains to the control strategy of squat assistance, which should be adaptable to various postures and cadences for different individuals. In particular, the effect of controlling negative power assistance during the squat-down phase is not studied.

METHODS

To fill these two gaps, first, we develop a simple (computationally efficient and implementable in a microcontroller) and generalizable (for different postures, cadences, and individuals) torque controller for portable knee exoskeletons that delivers both negative and positive power. Our portable knee exoskeleton can benefit users by enhancing efficiency (reducing metabolic cost, heart rate, breathing ventilation), mitigating muscle strain (reducing EMG), and reducing perceived exertion (reducing Borg 6-20 scale) during squatting. Second, we study the effect of three levels of negative power assistance during the squat-down phase.

RESULTS

This study integrates comprehensive biomechanics and physiology analyses that evaluate our exoskeleton's effectiveness using four objective and two subjective metrics with a group of able-bodied subjects (n = 7). The exoskeleton reduced metabolic cost by 12.8%, heart rate by 13.8%, breathing ventilation by 8.9%, and reduced extensor muscle activity by 39.4-43.2%, flexor muscle activity by 18.9-20.3%, and Borg perceived exertion rate by 1.8 during squatting compare with not wearing the robot.

CONCLUSION

Different from the musculoskeletal model predictions that suggest increasing benefit with a higher level of negative power assistance, we find that the best performances were achieved with a moderate level of negative power assistance, followed by no assistance and then high assistance.

A Review of Wearable Back-Support Exoskeletons for Preventing Work-Related Musculoskeletal Disorders

Long-term manual material handling (MMH) work leads to the trend of the younger onset of work-related musculoskeletal disorders (WMSDs), with low back pain (LBP) being the most common, which causes great trouble for both society and patients. To effectively prevent LBP and provide support for workers engaged in MMH work, wearable lumbar assistive exoskeletons have played a key role in industrial scenarios. This paper divides wearable lumbar assistive exoskeletons into powered, unpowered, and quasi-passive types, systematically reviews the research status of each type of exoskeleton, and compares and discusses the key factors such as driving mode, mechanical structure, control strategy, performance evaluation, and human-machine interaction. It is found that many studies focus on the assistive performance, human-machine coupling coordination, and adaptability of wearable lumbar assistive exoskeletons. At the same time, the analysis results show that there are many types of performance evaluation indicators, but a unified and standardized evaluation method and system are still lacking. This paper analyzes current research findings, identifies existing issues, and provides recommendations for future research. This study provides a theoretical basis and design ideas for the development of wearable lumbar assistive exoskeleton systems.

Performance Evaluation of Monocular Markerless Pose Estimation Systems for Industrial Exoskeletons

Industrial exoskeletons (a.k.a. wearable robots) have been developed to reduce musculoskeletal fatigue and work injuries. Human joint kinematics and human-robot alignment are important measurements in understanding the effects of industrial exoskeletons. Recently, markerless pose estimation systems based on monocular color (red, green, blue-RGB) and depth cameras are being used to estimate human joint positions. This study analyzes the performance of monocular markerless pose estimation systems on human skeletal joint estimation while wearing exoskeletons. Two pose estimation systems producing RGB and depth images from ten viewpoints are evaluated for one subject in 14 industrial poses. The experiment was repeated for three different types of exoskeletons on the same subject. An optical tracking system (OTS) was used as a reference system. The image acceptance rate was 56% for the RGB, 22% for the depth, and 78% for the OTS pose estimation system. The key sources of pose estimation error were the occlusions from the exoskeletons, industrial poses, and viewpoints. The reference system showed decreased performance when the optical markers were occluded by the exoskeleton or when the markers' position shifted with the exoskeleton. This study performs a systematic comparison of two types of monocular markerless pose estimation systems and an optical tracking system, as well as a proposed metric, based on a tracking quality ratio, to assess whether a skeletal joint estimation would be acceptable for human kinematics analysis in exoskeleton studies.

Human activity recognition algorithms for manual material handling activities

Human Activity Recognition (HAR) using wearable sensors has prompted substantial interest in recent years due to the availability and low cost of Inertial Measurement Units (IMUs). HAR using IMUs can aid both the ergonomic evaluation of the performed activities and, more recently, with the development of exoskeleton technologies, can assist with the selection of precisely tailored assisting strategies. However, there needs to be more research regarding the identification of diverse lifting styles, which requires appropriate datasets and the proper selection of hyperparameters for the employed classification algorithms. This paper offers insight into the effect of sensor placement, number of sensors, time window, classifier complexity, and IMU data types used in the classification of lifting styles. The analyzed classifiers are feedforward neural networks, 1-D convolutional neural networks, and recurrent neural networks, standard architectures in time series classification but offer different classification capabilities and computational complexity. This is of the utmost importance when inference is expected to occur in an embedded platform such as an occupational exoskeleton. It is shown that accurate lifting style detection requires multiple sensors, sufficiently long time windows, and classifier architectures able to leverage the temporal nature of the data since the differences are subtle from a kinematic point of view but significantly impact the possibility of injuries.

Soft back exosuit controlled by neuro-mechanical modeling provides adaptive assistance while lifting unknown loads and reduces lumbosacral compression forces

State-of-the-art controllers for active back exosuits rely on body kinematics and state machines. These controllers do not continuously target the lumbosacral compression forces or adapt to unknown external loads. The use of additional contact or load detection could make such controllers more adaptive; however, it can be impractical for daily use. Here, we developed a novel neuro-mechanical model-based controller (NMBC) that uses a personalized electromyography (EMG)-driven musculoskeletal (MSK) model to estimate lumbosacral joint loading. NMBC provided adaptive, subject- and load-specific assistive forces proportional to estimates of the active part of biological joint moments through a soft back support exosuit. Without a priori information, the maximum assistive forces of the cable were modulated across weights. Simultaneously, we applied a non-adaptive, kinematic-dependent, trunk inclination-based controller (TIBC). Both NMBC and TIBC reduced the mean and peak biomechanical metrics, although not all reductions were significant. TIBC did not modulate assistance across weights. NMBC showed larger reductions of mean than peak values, significant reductions during the erect stance and the cumulative compressive loads by 21% over multiple cycles in a cohort of 10 participants. Overall, NMBC targeted mean lumbosacral compressive forces during lifting without a priori information of the load being carried. This may facilitate the adoption of non-hindering wearable robotics in real-life scenarios. As NMBC is informed by an EMG-driven MSK model, it is possible to tune the timing of NMBC-generated torque commands to the exosuit (delaying or anticipating commands with respect to biological torques) to target further reduction of peak or mean compressive forces and muscle fatigue.

L-GABS: Parametric Modeling of a Generic Active Lumbar Exoskeleton for Ergonomic Impact Assessment

Companies increasingly implement exoskeletons in their production lines to reduce musculoskeletal disorders. Studies have been conducted on the general ergonomic effects of exoskeletons in production environments; however, it remains challenging to predict the biomechanical effects these devices may have in specific jobs. This article proposes the parametric modeling of an active lumbar exoskeleton using the Forces ergonomic method, which calculates the ergonomic risk using motion capture in the workplace, considering the internal joint forces. The exoskeleton was studied to model it in the Forces method using a four-phase approach based on experimental observations (Phase 1) and objective data collection via motion capture with inertial sensors and load cells for lifting load movements. From the experimentation the angles of each body segment, the effort perceived by the user, and the activation conditions were obtained (Phase 2). After modeling development (Phase 3), the experimental results regarding the force and risk were evaluated obtaining differences between model and experimental data of 0.971 ± 0.171 kg in chest force and 1.983 ± 0.678% in lumbar risk (Phase 4). This approach provides a tool to evaluate the biomechanical effects of this device in a work task, offering a parametric and direct approximation of the effects prior to implementation.

A comprehensive assessment of a passive back support exoskeleton for load handling assistance

Various back support exoskeletons (BSEs) have been developed to reduce the workload and the risk of musculoskeletal disorders. However, the evaluations of such devices have primarily focused on specific quantitative aspects like muscle activation level or metabolic cost without any assessment of the user perception or comfort. In addition, the absence of an universal guidance or agreement on the methods for quantifying the efficacy of exoskeletons has hampered a systematic comparison among the developed devices. This study introduces a newly developed passive BSE for heavy load handling workers, and verifies its assistive effect through a rigorous and multifaceted evaluation. Fifteen young and healthy males participated in two experiment sessions. In the first session, participants lifted a 15 kg box and held it in a static position. In the second session, participants performed repetitive lifting tasks with a 10 kg box. The developed BSE reduced root mean square, peak, and integrated muscle activation with statistical significance in the key muscles. The BSE alleviated muscle fatigue by delaying spectral shift of instantaneous median frequency in the lumbar erector spinae (p < 0.001) and gluteus maximus (p < 0.001). The BSE also decreased energy expenditure by 13.6% (p < 0.001). In addition, the BSE reduced participants' rate of perceived exertion and local musculoskeletal discomfort by 14.7% (p = 0.005) and 30.5% (p = 0.001), respectively. These results support the efficacy of the developed BSE. The multifaceted evaluation process used in this study also contributes to proposing a systematic guidance on evaluating BSEs.

A repeated strike loading organ culture model for studying compression-associated chronic disc degeneration

Mechanical stress has been viewed as one of the key risk factors in accelerating the intervertebral disc degeneration process. The goal of the present study was to employ a repeated strike loading bovine caudal disc system to elucidate the pathophysiological impacts of cumulative mechanical stress on the disc. The discs in the model groups were subjected to two different mechanical stresses: one strike loading or repeated strike loading. The following indices were analyzed: histological morphology, glycosaminoglycan release, disc height, cell viability, apoptosis-related protein expression, and catabolism-related gene expression. Both mechanical stress modes induced degenerative changes in the discs by day 11, such as clefts and delamination of the annulus fibrosus; they increased glycosaminoglycan release. Cell viability was significantly decreased and catabolic gene expression was significantly up-regulated in the degenerative loading group and repeated strike loading group by day 9. These alterations remained evident in the annulus fibrosus tissue of the repeated strike loading group on day 11. Our data suggests that the repeated strike loading model adopted in this study could lead to degenerative changes in the disc organ model. Annulus fibrosus cells displayed a more noticeable response to mechanical stress damage and a slower recovery process, suggesting that the annulus fibrosus serves as a pivotal factor in disc degeneration due to mechanical stress injuries. The study also indicates that due to the gradual self-repair of intervertebral disc cells after injury, it is necessary to apply repeated strike loading on the disc at specific intervals when researching the repair of chronic disc injuries.

The effect of active exoskeleton support with different lumbar-to-hip support ratios on spinal musculoskeletal loading and lumbar kinematics during lifting

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.

A Neuromechanical Model-Based Strategy to Estimate the Operator's Payload in Industrial Lifting Tasks

One of the main technological barriers hindering the development of active industrial exoskeleton is today represented by the lack of suitable payload estimation algorithms characterized by high accuracy and low calibration time. The knowledge of the payload enables exoskeletons to dynamically provide the required assistance to the user. This work proposes a payload estimation methodology based on personalized Electromyography-driven musculoskeletal models (pEMS) combined with a payload estimation method we called "delta torque" that allows the decoupling of payload dynamical properties from human dynamical properties. The contribution of this work lies in the conceptualization of such methodology and its validation considering human operators during industrial lifting tasks. With respect to existing solutions often based on machine learning, our methodology requires smaller training datasets and can better generalize across different payloads and tasks. The proposed payload estimation methodology has been validated on lifting tasks with 0kg, 5kg, 10kg and 15kg, resulting in an average MAE of about 1.4 Kg. Even if 5kg and 10Kg lifting tasks were out of the training set, the MAE related to these tasks are 1.6 kg and 1.1 kg, respectively, demonstrating the generalizing property of the proposed methodology. To the best of the authors' knowledge, this is the first time that an EMG-driven model-based approach is proposed for human payload estimation.

A Novel Passive Back-Support Exoskeleton With a Spring-Cable-Differential for Lifting Assistance

Lower back injuries are the most common work-related musculoskeletal disorders. As a wearable device, a back-support exoskeleton (BSE) can reduce the risk of lower back injuries and passive BSEs can achieve a low device weight. However, with current passive BSEs, there is a problem that the user must push against the device when lifting the leg to walk, which is perceived as particularly uncomfortable due to the resistance. To solve this problem, we propose a novel passive BSE that can automatically distinguish between lifting and walking. A unique spring-cable-differential acts as a torque generator to drive both hip joints, providing adequate assistive torque during lifting and low resistance during walking. The optimization of parameters can accommodate the asymmetry of human gait. In addition, the assistive torque on both sides of the user is always the same to ensure the balance of forces. By using a cable to transmit the spring force, we placed the torque generator on the person's back to reduce the weight on the legs. To test the effectiveness of the device, we performed a series of simulated lifting tasks and walking trials. When lifting a load of 10 kg in a squatting and stooping position, the device was able to reduce the activation of the erector spinae muscles by up to 41%. No significant change in the activation of the leg and back muscles was detected during walking.

Effects of back-support exoskeletons with different functional mechanisms on trunk muscle activity and kinematics

Musculoskeletal disorders constitute the leading work-related health issue. Mechanical loading of the lower back contributes as a major risk factor and is prevalent in many tasks performed in logistics. The study aimed to compare acute effects of exoskeletons with different functional mechanisms in a logistic task. Twelve young, healthy individuals participated in the study. Five exoskeletons with different functional mechanisms were tested in a logistic task, consisting of lifting, carrying, and lowering a 13 kg box. By using electromyography (EMG), mean muscle activities of four muscles in the trunk were analyzed. Additionally, kinematics by task completion time and range of motion (RoM) of the major joints and segments were investigated. A main effect was found for Musculus erector spinae, Musculus multifidus, and Musculus latissimus dorsi showing differences in muscle activity reductions between exoskeletons. Reduction in ES mean activity compared to baseline was primarily during lifting from ground level. The exoskeletons SoftExo Lift and Cray X also showed ES mean reduction during lowering the box. Prolonged task duration during the lifting phase was found for the exoskeletons BionicBack, SoftExo Lift, and Japet.W. Japet.W showed a trend in reducing hip RoM during that phase. SoftExo Lift caused a reduction in trunk flexion during the lifting phase. A stronger trunk inclination was only found during lifting from the table for the SoftExo Lift and the Cray X. In conclusion, muscle activity reductions by exoskeleton use should not be assessed without taking their designed force paths into account to correctly interpret the effects for long-term injury prevention.

IMU-based human activity recognition and payload classification for low-back exoskeletons

Nowadays, work-related musculoskeletal disorders have a drastic impact on a large part of the world population. In particular, low-back pain counts as the leading cause of absence from work in the industrial sector. Robotic exoskeletons have great potential to improve industrial workers' health and life quality. Nonetheless, current solutions are often limited by sub-optimal control systems. Due to the dynamic environment in which they are used, failure to adapt to the wearer and the task may be limiting exoskeleton adoption in occupational scenarios. In this scope, we present a deep-learning-based approach exploiting inertial sensors to provide industrial exoskeletons with human activity recognition and adaptive payload compensation. Inertial measurement units are easily wearable or embeddable in any industrial exoskeleton. We exploited Long-Short Term Memory networks both to perform human activity recognition and to classify the weight of lifted objects up to 15 kg. We found a median F1 score of [Formula: see text] (activity recognition) and [Formula: see text] (payload estimation) with subject-specific models trained and tested on 12 (6M-6F) young healthy volunteers. We also succeeded in evaluating the applicability of this approach with an in-lab real-time test in a simulated target scenario. These high-level algorithms may be useful to fully exploit the potential of powered exoskeletons to achieve symbiotic human-robot interaction.

A systematic literature review of evidence for the use of assistive exoskeletons in defence and security use cases

Advances in assistive exoskeleton technology, and a boom in related scientific literature, prompted a need to review the potential use of exoskeletons in defence and security. A systematic review examined the evidence for successful augmentation of human performance in activities deemed most relevant to military tasks. Categories of activities were determined a priori through literature scoping and Human Factors workshops with military stakeholders. Workshops identified promising opportunities and risks for integration of exoskeletons into military use cases. The review revealed promising evidence for exoskeletons' capacity to assist with load carriage, manual lifting, and working with tools. However, the review also revealed significant gaps in exoskeleton capabilities and likely performance levels required in the use case scenarios. Consequently, it was recommended that a future roadmap for introducing exoskeletons to military environments requires development of performance criteria for exoskeletons that can be used to implement a human-centred approach to research and development.

Engineering design strategies for force augmentation exoskeletons: A general review

In the industrial and military sector, work activities are required transporting or supporting heavy loads manually, affecting this the human spinal column due to the weight of the loads or the repetition of this labor. In this regard, the use of force-enhancing exoskeletons is a potential solution to this issue. Therefore, this article summarizes the state of the art in relevant contributions to structural design, control systems, actuators, and performance metrics to evaluate the proper functioning of exoskeletons used for load support and transfer. This is essential to address current and new open problems in these applications, and this includes reducing the metabolic cost and enhancing the loading force in exoskeletons, in which challenges such as structural design and kinetic interactions between the human and the robot are presented. The systematic review of the strategies found in the literature helps addressing these challenges in an orderly way. The proposal of some alternative solutions could help to solving some of the challenges mentioned above, as well as further research to improve the design of these devices is necessary.

sEMG-Triggered Fast Assistance Strategy for a Pneumatic Back Support Exoskeleton

To prevent lower back pain (LBP) in the industrial workplace, various powered back support exoskeletons (BSEs) have been developed. However, conventional kinematics-triggered assistance (KA) strategies induce latency, degrading assistance efficiency. Therefore, we proposed and experimentally evaluated a surface electromyography (sEMG)-triggered assistance (EA) strategy. Nine healthy subjects participated in the lifting experiments: 1) external loads test, 2) extra latency test, and 3) repetitive lifting test. In the external loads test, subject performed lifting with four different external loads (0 kg, 7.5 kg, 15 kg, and 22.5 kg). The assistance was triggered earlier by EA compared to KA from 114 ms to 202 ms, 163 ms to 269 ms for squat and stoop lifting respectively, as external loads increased from 0 kg to 22.5 kg. In the extra latency test, the effects of extra latency (manual switch, 0 ms, 100 ms and 200 ms) in EA on muscle activities were investigated. Muscle activities were minimized in the fast assistance (0 ms and 100 ms) condition and increased with extra latency. In the repetitive lifting test, the EA strategy significantly reduced L1 muscle fatigue by 70.4% in stoop lifting, compared to KA strategy. Based on the experimental results, we concluded that fast assistance triggered by sEMG improved assistance efficiency in BSE and was particularly beneficial in heavy external loads situations. The proposed assistive strategy can be used to prevent LBP by reducing back muscle fatigue and is easily applicable to various industrial exoskeleton applications.

Exoskeleton Application to Military Manual Handling Tasks

OBJECTIVE

The aim of this review was to determine how exoskeletons could assist Australian Defence Force personnel with manual handling tasks.

BACKGROUND

Musculoskeletal injuries due to manual handling are physically damaging to personnel and financially costly to the Australian Defence Force. Exoskeletons may minimize injury risk by supporting, augmenting, and/or amplifying the user's physical abilities. Exoskeletons are therefore of interest in determining how they could support the unique needs of military manual handling personnel.

METHOD

Industrial and military exoskeleton studies from 1990 to 2019 were identified in the literature. This included 67 unique exoskeletons, for which Information about their current state of development was tabulated.

RESULTS

Exoskeleton support of manual handling tasks is largely through squat/deadlift (lower limb) systems (64%), with the proposed use case for these being load carrying (42%) and 78% of exoskeletons being active. Human-exoskeleton analysis was the most prevalent form of evaluation (68%) with reported reductions in back muscle activation of 15%-54%.

CONCLUSION

The high frequency of citations of exoskeletons targeting load carrying reflects the need for devices that can support manual handling workers. Exoskeleton evaluation procedures varied across studies making comparisons difficult. The unique considerations for military applications, such as heavy external loads and load asymmetry, suggest that a significant adaptation to current technology or customized military-specific devices would be required for the introduction of exoskeletons into a military setting.

APPLICATION

Exoskeletons in the literature and their potential to be adapted for application to military manual handling tasks are presented.

Trends in Workplace Wearable Technologies and Connected‐Worker Solutions for Next‐Generation Occupational Safety, Health, and Productivity

The workplace influences the safety, health, and productivity of workers at multiple levels. To protect and promote total worker health, smart hardware, and software tools have emerged for the identification, elimination, substitution, and control of occupational hazards. Wearable devices enable constant monitoring of individual workers and the environment, whereas connected worker solutions provide contextual information and decision support. Here, the recent trends in commercial workplace technologies to monitor and manage occupational risks, injuries, accidents, and diseases are reviewed. Workplace safety wearables for safe lifting, ergonomics, hazard identification, sleep monitoring, fatigue management, and heat and cold stress are discussed. Examples of workplace productivity wearables for asset tracking, augmented reality, gesture and motion control, brain wave sensing, and work stress management are given. Workplace health wearables designed for work‐related musculoskeletal disorders, functional movement disorders, respiratory hazards, cardiovascular health, outdoor sun exposure, and continuous glucose monitoring are shown. Connected worker platforms are discussed with information about the architecture, system modules, intelligent operations, and industry applications. Predictive analytics provide contextual information about occupational safety risks, resource allocation, equipment failure, and predictive maintenance. Altogether, these examples highlight the ground‐level benefits of real‐time visibility about frontline workers, work environment, distributed assets, workforce efficiency, and safety compliance.

Benchmarking occupational exoskeletons: An evidence mapping systematic review

OBJECTIVES

To provide an overview of protocols assessing the effect of occupational exoskeletons on users and to formulate recommendations towards a literature-based assessment framework to benchmark the effect of occupational exoskeletons on the user.

METHODS

PubMed (MEDLINE), Web of Science database and Scopus were searched (March 2, 2021). Studies were included if they investigated the effect of one or more occupational exoskeletons on the user.

RESULTS

In total, 139 eligible studies were identified, encompassing 33, 25 and 18 unique back, shoulder and other exoskeletons, respectively. Device validation was most frequently conducted using controlled tasks while collecting muscle activity and biomechanical data. As the exoskeleton concept matures, tasks became more applied and the experimental design more representative. With that change towards realistic testing environments came a trade-off with experimental control, and user experience data became more valuable.

DISCUSSION

This evidence mapping systematic review reveals that the assessment of occupational exoskeletons is a dynamic process, and provides literature-based assessment recommendations. The homogeneity and repeatability of future exoskeleton assessment experiments will increase following these recommendations. The current review recognises the value of variability in evaluation protocols in order to obtain an overall overview of the effect of exoskeletons on the users, but the presented framework strives to facilitate benchmarking the effect of occupational exoskeletons on the users across this variety of assessment protocols.

Design and Pilot Evaluation of a Prototype Sensorized Trunk Exoskeleton

Trunk exoskeletons are wearable devices that support wearers during physically demanding tasks by reducing biomechanical loads and increasing stability. In this paper, we present a prototype sensorized passive trunk exoskeleton, which includes five motion processing units (3-axis accelerometers and gyroscopes with onboard digital processing), four one-axis flex sensors along the exoskeletal spinal column, and two one-axis force sensors for measuring the interaction force between the wearer and exoskeleton. A pilot evaluation of the exoskeleton was conducted with two wearers, who performed multiple everyday tasks (sitting on a chair and standing up, walking in a straight line, picking up a box with a straight back, picking up a box with a bent back, bending forward while standing, bending laterally while standing) while wearing the exoskeleton. Illustrative examples of the results are presented as graphs. Finally, potential applications of the sensorized exoskeleton as the basis for a semi-active exoskeleton design or for audio/haptic feedback to guide the wearer are discussed.

Toward Real-Time Detection of Object Lifting Using Wearable Inertial Measurement Units

Back injuries and other occupational injuries are common in workers who engage in long, arduous physical labor. The risk of these injuries could be reduced using assistive devices that automatically detect an object lifting motion and support the user while they perform the lift; however, such devices must be able to detect the lifting motion as it occurs. We thus developed a system to detect the start and end of a lift (performed as a stoop or squat) in real time based on pelvic angle and the distance between the user's hands and the user's center of mass. The measurements were input to an algorithm that first searches for hand-center distance peaks in a sliding window, then checks the pelvic displacement angle to verify lift occurrence. The approach was tested with 5 participants, who performed a total of 100 lifts of four different types. The times of actual lifts were determined by manual video annotation. The median time error (absolute difference between detected and actual occurrence time) for lifts that were not false negatives was 0.11 s; a lift was considered a false negative if it was not detected within two seconds of it actually occurring. Furthermore, 95% of lifts that were detected occurred within 0.28 s of actual occurrence. This shows that it is possible to reliably detect lifts in real time based on the pelvic displacement angle and the distance between the user's hands and their center of mass.

Versatile and non-versatile occupational back-support exoskeletons: A comparison in laboratory and field studies

Assistive strategies for occupational back-support exoskeletons have focused, mostly, on lifting tasks. However, in occupational scenarios, it is important to account not only for lifting but also for other activities. This can be done exploiting human activity recognition algorithms that can identify which task the user is performing and trigger the appropriate assistive strategy. We refer to this ability as exoskeleton versatility. To evaluate versatility, we propose to focus both on the ability of the device to reduce muscle activation (efficacy) and on its interaction with the user (dynamic fit). To this end, we performed an experimental study involving healthy subjects replicating the working activities of a manufacturing plant. To compare versatile and non-versatile exoskeletons, our device, XoTrunk, was controlled with two different strategies. Correspondingly, we collected muscle activity, kinematic variables and users' subjective feedbacks. Also, we evaluated the task recognition performance of the device. The results show that XoTrunk is capable of reducing muscle activation by up to in lifting and in carrying. However, the non-versatile control strategy hindered the users' natural gait (e.g., reduction of hip flexion), which could potentially lower the exoskeleton acceptance. Detecting carrying activities and adapting the control strategy, resulted in a more natural gait (e.g., increase of hip flexion). The classifier analyzed in this work, showed promising performance (online accuracy > 91%). Finally, we conducted 9 hours of field testing, involving four users. Initial subjective feedbacks on the exoskeleton versatility, are presented at the end of this work.

Occupational exoskeletons: A roadmap toward large-scale adoption. Methodology and challenges of bringing exoskeletons to workplaces

The large-scale adoption of occupational exoskeletons (OEs) will only happen if clear evidence of effectiveness of the devices is available. Performing product-specific field validation studies would allow the stakeholders and decision-makers (e.g., employers, ergonomists, health, and safety departments) to assess OEs' effectiveness in their specific work contexts and with experienced workers, who could further provide useful insights on practical issues related to exoskeleton daily use. This paper reviews present-day scientific methods for assessing the effectiveness of OEs in laboratory and field studies, and presents the vision of the authors on a roadmap that could lead to large-scale adoption of this technology. The analysis of the state-of-the-art shows methodological differences between laboratory and field studies. While the former are more extensively reported in scientific papers, they exhibit limited generalizability of the findings to real-world scenarios. On the contrary, field studies are limited in sample sizes and frequently focused only on subjective metrics. We propose a roadmap to promote large-scale knowledge-based adoption of OEs. It details that the analysis of the costs and benefits of this technology should be communicated to all stakeholders to facilitate informed decision making, so that each stakeholder can develop their specific role regarding this innovation. Large-scale field studies can help identify and monitor the possible side-effects related to exoskeleton use in real work situations, as well as provide a comprehensive scientific knowledge base to support the revision of ergonomics risk-assessment methods, safety standards and regulations, and the definition of guidelines and practices for the selection and use of OEs.

Real-Time Hierarchical Classification of Time Series Data for Locomotion Mode Detection

OBJECTIVE

Accurate real-time estimation of motion intent is critical for rendering useful assistance using wearable robotic prosthetic and exoskeleton devices during user-initiated motions. We aim to evaluate hierarchical classification as a strategy for real-time locomotion mode recognition for the control of wearable robotic prosthetics and exoskeletons during user-intiated motions.

METHODS

We collect motion data from 8 subjects using a set of 7 inertial sensors for 16 lower limb locomotion modes of different specificities. A CNN based hierarchical classifier is trained to classify the modes into a specified label hierarchy. We measure the accuracy, stability, behaviour during mode transitions and suitability for real-time inference of the classifier.

RESULTS

The method achieves stable classification of locomotion modes using 1280 ms of time history data. It achieves average classification accuracy of 94.34% and an average AU(PRC) of 0.773 - comparable to similar classifiers. The method produces more informative classifications at transitions between modes. Less specific classes are classified earlier than more specific classes in the hierarchy. The inference step of the classifier can be executed in less than 2 ms on embedded hardware, indicating suitability for real-time operation.

CONCLUSION

Hierarchical classification can achieve accurate detection of locomotion modes and can break up mode transitions into multiple transitions between modes of different specificity.

SIGNIFICANCE

Multi-specific hierarchical classification of locomotion modes could lead to smoother, more fine grained control adaptation of wearable robots during locomotion mode transitions.

Effects of industrial back-support exoskeletons on body loading and user experience: an updated systematic review

This study is an updated systematic review of papers published in the last 5 years on industrial back-support exoskeletons. The research questions were aimed at addressing the recent findings regarding objective (e.g. body loading, user performance) and subjective evaluations (e.g. user satisfaction), potential side effects, and methodological aspects of usability testing. Thirteen studies of active and twenty of passive exoskeletons were identified. The exoskeletons were tested during lifting and bending tasks, predominantly in laboratory settings and among healthy young men. In general, decreases in participants' back-muscle activity, peak L5/S1 moments and spinal compression forces were reported. User endurance during lifting and static bending improved, but performance declined during tasks that required increased agility. The overall user satisfaction was moderate. Some side effects were observed, including increased abdominal/lower-limb muscle activity and changes in joint angles. A need was identified for further field studies, involving industrial workers, and reflecting actual work situations. Practitioner summary: Due to increased research activity in the field, a systematic review was performed of recent studies on industrial back-support exoskeletons, addressing objective and subjective evaluations, side effects, and methodological aspects of usability testing. The results indicate the efficiency of exoskeletons in back-load reduction and a need for further studies in real work situations. Abbrevaitions: BB: biceps brachii; BF: biceps femoris; CoM: centre of mass; DA: deltoideus anterior; EMG: electromyography; ES: erector spinae; ES-C: erector spinae-cervical; ESI: erector spinae iliocostalis; ESI-L: erector spinae iliocostalis-lumborum; ESL: erector spinae longissimus; ES-L: erector spinae-lumbar; ESL-L: erector spinae longissimus-lumborum; ESL-T: erector spinae longissimus-thoracis; ES-T: erector spinae-thoracic; GM: glutaeus maximus; LBP: low back pain; LD: latissimus dorsi; LPD: local perceived discomfort scale; LPP: local perceived pressure scale; MS: multifidus spinae; MSD: musculoskeletal disorder; M-SFS: modified spinal function sort; NMV: no mean value provided; OA: obliquus abdominis (internus and externus); OEA: obliquus externus abdominis; OIA : obliquus internus abdominis; RA: rectus abdominis; RF: rectus femoris; RoM: range of motion; SUS: system usability scale; T: trapezius (pars Ascendens and Descendens); TA: trapezius pars ascendens; TC: mid-cervical trapezius; TD: trapezius pars descendens; VAS: visual analog scale; VL: vastus lateralis; VM: vastus medialis.

Locomotion Mode Recognition with Inertial Signals for Hip Joint Exoskeleton

Recognizing locomotion modes is a crucial step in controlling lower-limb exoskeletons/orthoses. Our study proposed a fuzzy-logic-based locomotion mode/transition recognition approach that uses the onrobot inertial sensors for a hip joint exoskeleton (active pelvic orthosis). The method outputs the recognition decisions at each extreme point of the hip joint angles purely relying on the integrated inertial sensors. Compared with the related studies, our approach enables calibrations and recognition without additional sensors on the feet. We validated the method by measuring four locomotion modes and eight locomotion transitions on three able-bodied subjects wearing an active pelvic orthosis (APO). The average recognition accuracy was 92.46% for intrasubject crossvalidation and 93.16% for intersubject crossvalidation. The average time delay during the transitions was 1897.9 ms (28.95% one gait cycle). The results were at the same level as the related studies. On the other side, the study is limited in the small sample size of the subjects, and the results are preliminary. Future efforts will be paid on more extensive evaluations in practical applications.

Evaluation of an acceleration-based assistive strategy to control a back-support exoskeleton for manual material handling

To reduce the incidence of occupational musculoskeletal disorders, back-support exoskeletons are being introduced to assist manual material handling activities. Using a device of this type, this study investigates the effects of a new control strategy that uses the angular acceleration of the user's trunk to assist during lifting tasks. To validate this new strategy, its effectiveness was experimentally evaluated relative to the condition without the exoskeleton as well as against existing strategies for comparison. Using the exoskeleton during lifting tasks reduced the peak compression force on the L5S1 disc by up to 16%, with all the control strategies. Substantial differences between the control strategies in the reductions of compression force, lumbar moment and back muscle activation were not observed. However, the new control strategy reduced the movement speed less with respect to the existing strategies. Thanks to improved timing in the assistance in relation to the typical dynamics of the target task, the hindrance to typical movements appeared reduced, thereby promoting intuitiveness and comfort.

EMG Characterization and Processing in Production Engineering

Electromyography (EMG) signals are biomedical signals that measure electrical currents generated during muscle contraction. These signals are strongly influenced by physiological and anatomical characteristics of the muscles and represent the neuromuscular activities of the human body. The evolution of EMG analysis and acquisition techniques makes this technology more reliable for production engineering applications, overcoming some of its inherent issues. Taking as an example, the fatigue monitoring of workers as well as enriched human–machine interaction (HMI) systems used in collaborative tasks are now possible with this technology. The main objective of this research is to evaluate the current implementation of EMG technology within production engineering, its weaknesses, opportunities, and synergies with other technologies, with the aim of developing more natural and efficient HMI systems that could improve the safety and productivity within production environments.

Applicability of an Active Back-Support Exoskeleton to Carrying Activities

Occupational back-support exoskeletons are becoming a more and more common solution to mitigate work-related lower-back pain associated with lifting activities. In addition to lifting, there are many other tasks performed by workers, such as carrying, pushing, and pulling, that might benefit from the use of an exoskeleton. In this work, the impact that carrying has on lower-back loading compared to lifting and the need to select different assistive strategies based on the performed task are presented. This latter need is studied by using a control strategy that commands for constant torques. The results of the experimental campaign conducted on 9 subjects suggest that such a control strategy is beneficial for the back muscles (up to 12% reduction in overall lumbar activity), but constrains the legs (around 10% reduction in hip and knee ranges of motion). Task recognition and the design of specific controllers can be exploited by active and, partially, passive exoskeletons to enhance their versatility, i.e., the ability to adapt to different requirements.

Actuation Selection for Assistive Exoskeletons: Matching Capabilities to Task Requirements

Selecting actuators for assistive exoskeletons involves decisions in which designers usually face contrasting requirements. While certain choices may depend on the application context or design philosophy, it is generally desirable to avoid oversizing actuators in order to obtain more lightweight and transparent systems, ultimately promoting the adoption of a given device. In many cases, the torque and power requirements can be relaxed by exploiting the contribution of an elastic element acting in mechanical parallel. This contribution considers one such case and introduces a methodology for the evaluation of different actuator choices resulting from the combination of different motors, reduction gears, and parallel stiffness profiles, helping to match actuator capabilities to the task requirements. Such methodology is based on a graphical tool showing how different design choices affect the actuator as a whole. To illustrate the approach, a back-support exoskeleton for lifting tasks is considered as a case study.

Pneumatic Supply System Parameter Optimization for Soft Actuators

Soft actuators using pressurized air are being widely used due to their inherent compliance, conformability, and customizability. These actuators are powered and controlled by pneumatic supply systems (PSSs) consisting of components such as compressors, valves, tubing, and reservoirs. Regardless of the choice of actuator, the PSS critically affects overall performance of soft robots because it governs the soft actuator pressure dynamics, and thereby, the general dynamic behavior. While selecting and controlling PSS components for meeting desired soft actuator performance, specifications such as PSS mass, volume, and duration of operation must also be considered. Currently, there is no comprehensive study on PSS optimization for meeting dynamic performance and PSS specifications, due to limited understanding of soft actuator pressure dynamics, large solution space for PSSs, and variability in soft actuators. By considering critical parameters of PSS and soft actuators, we introduce and demonstrate PSS parameter optimization. We propose a normalized model for soft actuator pressure dynamics and quantify the relationship between PSS parameters, soft actuator design parameters, and dynamic performance metrics of rise time, fall time, and actuation frequency. After experimental validation, we applied these results and optimally select and control PSS components to meet desired soft actuator performance for a soft exosuit, while minimizing mass of selected components. The measured pressure response with this prototype agrees well with simulations, with root mean square errors <5.2%. This work is a step toward furthering the scope of soft robotics, as it enables PSS modeling and optimization, for meeting the desired soft actuator performance while also addressing PSS specifications.

Quasi-Direct Drive Actuation for a Lightweight Hip Exoskeleton with High Backdrivability and High Bandwidth

High-performance actuators are crucial to enable mechanical versatility of wearable robots, which are required to be lightweight, highly backdrivable, and with high bandwidth. State-of-the-art actuators, e.g., series elastic actuators (SEAs), have to compromise bandwidth to improve compliance (i.e., backdrivability). We describe the design and human-robot interaction modeling of a portable hip exoskeleton based on our custom quasi-direct drive (QDD) actuation (i.e., a high torque density motor with low ratio gear). We also present a model-based performance benchmark comparison of representative actuators in terms of torque capability, control bandwidth, backdrivability, and force tracking accuracy. This paper aims to corroborate the underlying philosophy of "design for control", namely meticulous robot design can simplify control algorithms while ensuring high performance. Following this idea, we create a lightweight bilateral hip exoskeleton to reduce joint loadings during normal activities, including walking and squatting. Experiments indicate that the exoskeleton is able to produce high nominal torque (17.5 Nm), high backdrivability (0.4 Nm backdrive torque), high bandwidth (62.4 Hz), and high control accuracy (1.09 Nm root mean square tracking error, 5.4% of the desired peak torque). Its controller is versatile to assist walking at different speeds and squatting. This work demonstrates performance improvement compared with state-of-the-art exoskeletons.

Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons

Research and development of active and passive exoskeletons for preventing work related injuries has steadily increased in the last decade. Recently, new types of quasi-passive designs have been emerging. These exoskeletons use passive viscoelastic elements, such as springs and dampers, to provide support to the user, while using small actuators only to change the level of support or to disengage the passive elements. Control of such devices is still largely unexplored, especially the algorithms that predict the movement of the user, to take maximum advantage of the passive viscoelastic elements. To address this issue, we developed a new control scheme consisting of Gaussian mixture models (GMM) in combination with a state machine controller to identify and classify the movement of the user as early as possible and thus provide a timely control output for the quasi-passive spinal exoskeleton. In a leave-one-out cross-validation procedure, the overall accuracy for providing support to the user was 86.72±0.86% (mean ± s.d.) with a sensitivity and specificity of 97.46±2.09% and 83.15±0.85% respectively. The results of this study indicate that our approach is a promising tool for the control of quasi-passive spinal exoskeletons.

Characterizing Human Box-Lifting Behavior Using Wearable Inertial Motion Sensors

Although several studies have used wearable sensors to analyze human lifting, this has generally only been done in a limited manner. In this proof-of-concept study, we investigate multiple aspects of offline lift characterization using wearable inertial measurement sensors: detecting the start and end of the lift and classifying the vertical movement of the object, the posture used, the weight of the object, and the asymmetry involved. In addition, the lift duration, horizontal distance from the lifter to the object, the vertical displacement of the object, and the asymmetric angle are computed as lift parameters. Twenty-four healthy participants performed two repetitions of 30 different main lifts each while wearing a commercial inertial measurement system. The data from these trials were used to develop, train, and evaluate the lift characterization algorithms presented. The lift detection algorithm had a start time error of 0.10 s ± 0.21 s and an end time error of 0.36 s ± 0.27 s across all 1489 lift trials with no missed lifts. For posture, asymmetry, vertical movement, and weight, our classifiers achieved accuracies of 96.8%, 98.3%, 97.3%, and 64.2%, respectively, for automatically detected lifts. The vertical height and displacement estimates were, on average, within 25 cm of the reference values. The horizontal distances measured for some lifts were quite different than expected (up to 14.5 cm), but were very consistent. Estimated asymmetry angles were similarly precise. In the future, these proof-of-concept offline algorithms can be expanded and improved to work in real-time. This would enable their use in applications such as real-time health monitoring and feedback for assistive devices.

BPNN-Based Real-Time Recognition of Locomotion Modes for an Active Pelvis Orthosis with Different Assistive Strategies

Real-time human intent recognition is important for controlling low-limb wearable robots. In this paper, to achieve continuous and precise recognition results on different terrains, we propose a real-time training and recognition method for six locomotion modes including standing, level ground walking, ramp ascending, ramp descending, stair ascending and stair descending. A locomotion recognition system is designed for the real-time recognition purpose with an embedded BPNN-based algorithm. A wearable powered orthosis integrated with this system and two inertial measurement units is used as the experimental setup to evaluate the performance of the designed method while providing hip assistance. Experiments including on-board training and real-time recognition parts are carried out on three able-bodied subjects. The overall recognition accuracies of six locomotion modes based on subject-dependent models are 98.43% and 98.03% respectively, with the wearable orthosis in two different assistance strategies. The cost time of recognition decision delivered to the orthosis is about 0.9[Formula: see text]ms. Experimental results show an effective and promising performance of the proposed method to realize real-time training and recognition for future control of low-limb wearable robots assisting users on different terrains.

Assessment of an On-board Classifier for Activity Recognition on an Active Back-Support Exoskeleton

Despite the growing interest, the adoption of industrial exoskeletons may still be held back by technical limitations. To enhance versatility and promote adoption, one aspect of interest could be represented by the potential of active and quasi-passive devices to automatically distinguish different activities and adjust their assistive profiles accordingly. This contribution focuses on an active back-support exoskeleton and extends previous work proposing the use of a Support Vector Machine to classify walking, bending and standing. Thanks to the introduction of a new feature-forearm muscle activity-this study shows that it is possible to perform reliable online classification. As a consequence, the authors introduce a new hierarchically-structured controller for the exoskeleton under analysis.

State-of-the-art research in robotic hip exoskeletons: A general review

Ageing population is now a global challenge, where physical deterioration is the common feature in elderly people. In addition, the diseases, such as spinal cord injury, stroke, and injury, could cause a partial or total loss of the ability of human locomotion. Thus, assistance is necessary for them to perform safe activities of daily living. Robotic hip exoskeletons are able to support ambulatory functions in elderly people and provide rehabilitation for the patients with gait impairments. They can also augment human performance during normal walking, loaded walking, and manual handling of heavy-duty tasks by providing assistive force/torque. In this article, a systematic review of robotic hip exoskeletons is presented, where biomechanics of the human hip joint, pathological gait pattern, and common approaches to the design of robotic hip exoskeletons are described. Finally, limitations of the available robotic hip exoskeletons and their possible future directions are discussed, which could serve a useful reference for the engineers and researchers to develop robotic hip exoskeletons with practical and plausible applications in geriatric orthopaedics.

The translational potential of this article

The past decade has witnessed a remarkable progress in research and development of robotic hip exoskeletons. Our aim is to summarize recent developments of robotic hip exoskeletons for the engineers, clinician scientists and rehabilitation personnel to develop efficient robotic hip exoskeletons for practical and plausible applications.

Acceleration-based Assistive Strategy to Control a Back-support Exoskeleton for Load Handling: Preliminary Evaluation

Industrial active exoskeletons have recently achieved considerable interest, due to their intrinsic versatility compared to passive devices. To achieve this versatility, an important open challenge is the design of appropriate control strategies to automatically modulate the physical assistance according to the activity the user is performing.This work focuses on active back-support exoskeletons. To improve the assistance provided in dynamic situations with respect to state-of-the-art methods, a new strategy making use of the angular acceleration of the user's trunk is presented.The feasibility and effectiveness of the proposed strategy were tested experimentally on a prototype in a load handling task. The main advantages in terms of assistive torque profiles emerge during the transition phases of the movement (i.e. beginning and end of lowering and lifting) indicating an appropriate adaptation to the dynamics of the execution.In this preliminary evaluation, the data on peak muscular activity at the spine show promising trends, encouraging further developments and a more detailed evaluation.

Classification of Lifting Techniques for Application of A Robotic Hip Exoskeleton

The number of exoskeletons providing load-lifting assistance has significantly increased over the last decade. In this field, to take full advantage of active exoskeletons and provide appropriate assistance to users, it is essential to develop control systems that are able to reliably recognize and classify the users' movement when performing various lifting tasks. To this end, the movement-decoding algorithm should work robustly with different users and recognize different lifting techniques. Currently, there are no studies presenting methods to classify different lifting techniques in real time for applications with lumbar exoskeletons. We designed a real-time two-step algorithm for a portable hip exoskeleton that can detect the onset of the lifting movement and classify the technique used to accomplish the lift, using only the exoskeleton-embedded sensors. To evaluate the performance of the proposed algorithm, 15 healthy male subjects participated in two experimental sessions in which they were asked to perform lifting tasks using four different techniques (namely, squat lifting, stoop lifting, left-asymmetric lifting, and right-asymmetric lifting) while wearing an active hip exoskeleton. Five classes (the four lifting techniques plus the class "no lift") were defined for the classification model, which is based on a set of rules (first step) and a pattern recognition algorithm (second step). Leave-one-subject-out cross-validation showed a recognition accuracy of 99.34 ± 0.85%, and the onset of the lift movement was detected within the first 121 to 166 ms of movement.

Mechanical Design and Control Strategy for Hip Joint Power Assisting

The basic requirements for mechanical design and control strategy are adapting to human joint movements and building an interaction model between human and robot. In this paper, a 3-UPS parallel mechanism is adopted to realize that the instantaneous rotation center of the assistive system coincides with human joint movement center, and a force sensory system is used to detect human movement intention and build the modeling of control strategy based on the interactive force. Then, based on the constructed experimental platform, the feasibility of movement intention detection and power assisting are verified through the experimental results.