Low-back pain is a common occupational hazard for industrial workers. Several studies show the advantages of using rigid and soft back-support passive exoskeletons and exosuits (exos) to reduce the low-back loading and risk of injury. However, benefits of using these exos have been shown to be task-specific. Therefore, in this study, we developed a benchmarking approach to assess exos for an industrial workplace at Hankamp Gears B.V. We assessed two rigid (Laevo Flex, Paexo back) and two soft (Auxivo Liftsuit 1.0, and Darwing Hakobelude) exos for tasks resembling the workplace. We measured the assistive moment provided by each exo and their respective influence on muscle activity as well as the user’s perception of comfort and exertion. Ten participants performed four lifting tasks (Static hold, Asymmetric, Squat, and Stoop), while their electromyography and subjective measures were collected. The two rigid exos provided the largest assistance during the Dynamic tasks. Reductions in erector spinae activity were seen to be task-specific, with larger reductions for the two rigid exos. Overall, Laevo Flex offered a good balance between assistive moments, reductions in muscle activity, as well as user comfort and reductions in perceived exertion. Thus, we recommend benchmarking exos for intended use in the industrial workplace. This will hopefully result in a better adoption of the back-support exoskeletons in the workplace and help reduce low-back pain.

This article examines the feasibility of employing a cold-gas thruster (CGT), intended as a backpack-wearable device, for purposes of arresting backward falls, and in particular describes a supervisory controller that, for some motion described by an arbitrary combination of center-of-mass angle and angular velocity, both detects an impending fall and determines when to initiate thrust in the CGT in order to arrest the impending fall. The CGT prototype and the supervisory controller are described and experimentally assessed using a rocking block apparatus intended to approximate a backward-falling human. In these experiments, the CGT and supervisory controller restored upright stability to the rocking block in all experiment cases that would have otherwise resulted in a fall without the CGT assistance. Since the controller and experiments employ a reduced-order model of a falling human, the authors also conducted a series of simulations intended to examine the extent to which the controller might remain effective in the case of a multi-segment human. The results of these simulations suggest that the CGT controller would be nearly as effective on a multi-segment falling human as on the reduced-order model.

Back overuse injuries are a significant problem in the U.S. Army, responsible for nearly a quarter of musculoskeletal injuries. Back exosuits are wearable devices that relieve musculoskeletal strain, make lifting easier, and could potentially reduce Soldier overuse injuries. But published studies have not evaluated exosuits during realistic field operations to assess acceptability to Soldiers. We tested a back exosuit on field artillery Soldiers during a field training exercise. Afterward, Soldiers completed a survey to quantify their satisfaction, intent to use, and performance impact of the exosuit. Feedback was overwhelmingly positive: Approximately 90% of Soldiers reported that exosuits increased their ability to perform their duties, and 100% said that if the exosuit were further developed and made available to them, they would be likely to wear it. These numerical survey results indicated that exosuits can provide a practical and acceptable way to assist lifting and augment physical performance during realistic Army operations without interfering with other duties.

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.

Cumulative back muscle fatigue plays a role in the occurrence of low-back injuries in occupations that require repetitive lifting of heavy loads and working in forward leaning postures. Lift-support exoskeletons have the potential to reduce back and hip muscle activity, thereby delaying the onset of fatigue in these muscles. Therefore, exoskeletons are being considered a potentially important tool to further reduce workload-related injuries. However, today no standards have been established on how to benchmark the support level of lift-support exoskeletons. This work proposes an experimental protocol to quantify the support level of a lift-support exoskeletons on instant changes in muscle activity and fatigue development while maintaining a static forward leaning posture. It then applies the protocol to experimentally assess the effect of the support provided by a commercially available lift-support exoskeleton, the LiftSuit 2.0 (Auxivo AG, Schwerzenbach, Switzerland), on the user. In a sample of 14 participants, the amplitude of the muscle activity of the back muscles $ \left({\Delta}_{\mathrm{erectorspinae},\mathrm{thoracic}}\hskip0.35em =\hskip0.35em 33.0\%,{\Delta}_{\mathrm{erectorspinae},\mathrm{lumbar}}\hskip0.35em =\hskip0.35em 13.2\%\right) $ and hip muscles ( $ {\Delta}_{\mathrm{gluteusmaximus}}\hskip0.35em =\hskip0.35em 16.3\% $ ) was significantly reduced. Wearing the exoskeleton significantly reduced the amount of fatigue developed during the task ( $ {\Delta}_{\mathrm{quadratuslumborum}}\hskip0.35em =\hskip0.35em 10.1\%,{\Delta}_{\mathrm{gluteusmaximus}}\hskip0.35em =\hskip0.35em 44.0\% $ ). Changes in muscle fatigue can be objectively recorded and correlated with relevant changes for exoskeleton users: the time a task can be performed and perceived low-back fatigue. Thus, including such measures of fatigue in standardized benchmarking procedures will help quantify the benefits of exoskeletons for occupational use.

Assistive forces transmitted from wearable robots to the robot’s users are often defined by controllers that rely on the accurate estimation of the human posture. The compliant nature of the human–robot interface can negatively affect the robot’s ability to estimate the posture. In this article, we present a novel algorithm that uses machine learning to correct these errors in posture estimation. For that, we recorded motion capture data and robot performance data from a group of participants (n = 8; 4 females) who walked on a treadmill while wearing a wearable robot, the Myosuit. Participants walked on level ground at various gait speeds and levels of support from the Myosuit. We used optical motion capture data to measure the relative displacement between the person and the Myosuit. We then combined this data with data derived from the robot to train a model, using a grading boosting algorithm (XGBoost), that corrected for the mechanical compliance errors in posture estimation. For the Myosuit controller, we were particularly interested in the angle of the thigh segment. Using our algorithm, the estimated thigh segment’s angle RMS error was reduced from 6.3° (2.3°) to 2.5° (1.0°), mean (standard deviation). The average maximum error was reduced from 13.1° (4.9°) to 5.9° (2.1°). These improvements in posture estimation were observed for all of the considered assistance force levels and walking speeds. This suggests that ML-based algorithms provide a promising opportunity to be used in combination with wearable-robot sensors for an accurate user posture estimation.

Hemiparetic walking after stroke is typically slow, asymmetric, and inefficient, significantly impacting activities of daily living. Extensive research shows that functional, intensive, and task-specific gait training is instrumental for effective gait rehabilitation, characteristics that our group aims to encourage with soft robotic exosuits. However, standard clinical assessments may lack the precision and frequency to detect subtle changes in intervention efficacy during both conventional and exosuit-assisted gait training, potentially impeding targeted therapy regimes. In this paper, we use exosuit-integrated inertial sensors to reconstruct three clinically meaningful gait metrics related to circumduction, foot clearance, and stride length. Our method corrects sensor drift using instantaneous information from both sides of the body. This approach makes our method robust to irregular walking conditions poststroke as well as usable in real-time applications, such as real-time movement monitoring, exosuit assistance control, and biofeedback. We validate our algorithm in eight people poststroke in comparison to lab-based optical motion capture. Mean errors were below 0.2 cm (9.9%) for circumduction, −0.6 cm (−3.5%) for foot clearance, and 3.8 cm (3.6%) for stride length. A single-participant case study shows our technique’s promise in daily-living environments by detecting exosuit-induced changes in gait while walking in a busy outdoor plaza.