Occupational exoskeletons and exosuits have been shown to reduce muscle demands and fatigue for physical tasks relevant to a variety of industries (e.g., logistics, construction, manufacturing, military, healthcare). However, adoption of these devices into the workforce has been slowed by practical factors related to comfort, form-factor, weight, and not interfering with movement or posture. We previously introduced a low-profile, dual-mode exosuit comprised of textile and elastic materials to address these adoption barriers. Here we build upon this prior work by introducing an extension mechanism that increases the moment arm of the exosuit while in engaged mode, then collapses in disengaged mode to retain key benefits related to being lightweight, low-profile, and unobstructive. Here we demonstrate both analytically and empirically how this extensible exosuit concept can (a) reduce device-to-body forces (which can improve comfort for some users and situations), or (b) increase the magnitude of torque assistance about the low back (which may be valuable for heavy-lifting jobs) without increasing shoulder or leg forces relative to the prior form-fitting exosuit. We also introduce a novel mode-switching mechanism, as well as a human-exosuit biomechanical model to elucidate how individual design parameters affect exosuit assistance torque and device-to-body forces. The proof-of-concept prototype, case study, and modeling work provide a foundation for understanding and implementing extensible exosuits for a broad range of applications. We envision promising opportunities to apply this new dual-mode extensible exosuit concept to assist heavy-lifting, to further enhance user comfort, and to address the unique needs of last-mile and other delivery workers.

Passive wearable exoskeletons are desirable as they can provide assistance during user movements while still maintaining a simple and low-profile design. These can be useful in industrial tasks where an ergonomic device could aid in load lifting without inconveniencing them and reducing fatigue and stress in the lower limbs. The SpringExo is a coil-spring design that aids in knee extension. In this paper, we describe the muscle activation of the knee flexors and extensors from seven healthy participants during repeated squats. The outcome measures are the timings of the key events during squat, flexion angle, muscle activation of rectus femoris and bicep femoris, and foot pressure characteristics of the participants. These outcome measures assess the possible effects of the device during lifting operations where reduced effort in the muscles is desired during ascent phase of the squat, without changing the knee and foot kinematics. The results show that the SpringExo significantly decreased rectus femoris activation during ascent (−2%) without significantly affecting either the bicep femoris or rectus femoris muscle activations in descent. This implies that the user could perform a descent without added effort and ascent with reduced effort. The exoskeleton showed other effects on the biomechanics of the user, increasing average squat time (+0.02 s) and maximum squat time (+0.1 s), and decreasing average knee flexion angle (−4°). The exoskeleton has no effect on foot loading or placement, that is, the user did not have to revise their stance while using the device.

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 $ 10 $ 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 $ 40\% $ in lifting and $ 30\% $ in carrying. However, the non-versatile control strategy hindered the users’ natural gait (e.g., $ -24\% $ 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., $ +9\% $ 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.

Although wearable robotic systems are designed to reduce the risk of low-back injury, it is unclear how effective assistance is, compared to improvements in lifting technique. We use a two-factor block study design to simulate how effective exoskeleton assistance and technical improvements are at reducing the risk of low-back injury when compared to a typical adult lifting a box. The effects of assistance are examined by simulating two different models: a model of just the human participant, and a model of the human participant wearing the SPEXOR exoskeleton. The effects of lifting technique are investigated by formulating two different types of optimal control problems: a least-squares problem which tracks the human participant’s lifting technique, and a minimization problem where the model is free to use a different movement. Different lifting techniques are considered using three different cost functions related to risk factors for low-back injury: cumulative low-back load (CLBL), peak low-back load (PLBL), and a combination of both CLBL and PLBL (HYB). The results of our simulations indicate that an exoskeleton alone can make modest reductions in both CLBL and PLBL. In contrast, technical improvements alone are effective at reducing CLBL, but not PLBL. The largest reductions in both CLBL and PLBL occur when both an exoskeleton and technical improvements are used. While all three of the lifting technique cost functions reduce both CLBL and PLBL, the HYB cost function offers the most balanced reduction in both CLBL and PLBL.