Portable active back support devices (BSDs) offer tunable assistance but are often bulky and heavy, limiting their usability. In contrast, passive BSDs are lightweight and compact but lack the ability to adapt their assistance to different back movements. We present a soft, lightweight, and compact BSD that combines a variable-stiffness passive element and an active element (an artificial muscle) in parallel. The device provides tunable assistance through discrete changes in stiffness values and active force levels. We validate the device's tuning capabilities through bench testing and on-body characterization. Further, we use the device's tuning capabilities to provide weight-adaptive object lifting and lowering assistance. We detect the weight handled by the user based on forearm force myography and upper-back inertial measurement unit data. Furthermore, electromyography analyses in five participants performing symmetric object lifting and lowering tasks showed reductions in back extensor activity. Preliminary results in one participant also indicated reduced muscle activity during asymmetric lifting.
Figures & tables
Figure 1: Overview. (a) Our proposed back support device combines a variable-stiffness passive element and an active element (a pneumatic artificial muscle) in parallel. (b) The device implements weight-adaptive assistance using discrete passive and active force profiles. (c) The lightweight and compact soft structure enables assistance during lifting and lowering, while allowing unrestricted mobility during activities such as sitting and walking.
Device
Active or Passive
Rigid or Soft
Force Generating Element
Weight (kg)
Peak Torque (Nm)
Torque Density (Nm/kg)
Weight Adaptive Profile
EMG Reduction (%)
[ 50 ]
Passive
Soft
Elastic band
1.4
35
25
No
NA
[ 26 ]
Passive
Rigid
Compliant mechanism
4
30
7.5
No
8
[ 52 ]
Passive
Rigid
Compliant mechanism
5
7.04
1.41
No
NA
[ 25 ]
Passive
Soft
Variable stiffness elastic band
1.2
20.4
17
No
NA
[ 32 ]
Active
Rigid
Direct drive motor
8
35
4.375
No
36.3
[ 34 ]
Active
Rigid
Direct drive motor
6
N/A
N/A
No
11.63
Table 1: Comparison of existing untethered BSDs.
Figure 2: (a) Device assembly with the VS resistance band (passive), IPAMs (active), and origami muscle (slack tuning). (b) Device and portable pneumatic/electronic units worn by a user.
Figure 3: (a) Design of the VS resistance band with an integrated electroadhesive (EA) clutch. (b) Principle of operation of a single interface of the EA clutch: When 300 V is applied between the positive and negative electrodes, the electro-adhesion at the interface prevents sliding. This reduces the active length of the resistance band and increases stiffness. The EA clutch includes eight such interfaces.
Figure 4: (a) IPAM design: It consists of a silicone tube helically wound with fiber reinforcement and sealed at both ends. Our device uses two such IPAMs in parallel. (b) Working principle of the IPAM: When pressurized, it extends axially; when deflated, it contracts, thereby generating an active force.
Figure 5: (a) Origami muscle assembly for slack tuning. (b) Working principle of the origami muscle: On vacuuming, a zigzag skeleton housed in a sealed fabric enclosure contracts, shortening the device. (c) Working principle of the EA muscle brakes: When 300 V is applied between the positive and negative electrodes, the electro-adhesion at the interface prevents sliding, locking the origami muscle length after slack tuning. This enables compact actuation without constant power draw. The EA muscle brakes include five EA interfaces on either side of the origami muscle.
Figure 6: (a) FMG armband design: Six FSRs mounted on PET substrates measure radial forearm pressure during weight handling. (b) An inextensible band wrapped over the elastic band limits excessive radial expansion of the elastic band when the user handles a weight. (c) FMG armband worn by a human subject.
Figure 7: (a) Pneumatic system schematic. (b) Truth table depicting the pneumatic control logic.
Figure 8: Schematic of the electronic system used to drive the EA clutch, the IPAMs, and the origami muscle assembly.
Figure 9: Analytical model of the VS resistance band: Spring-network model used to derive stiffness of the resistance band as a function of geometric and material parameters.
Figure 10: Second-order polynomial surface fit showing IPAM force as a function of elongation and pressure, obtained via bench testing.
Figure 11: Analytical model of the full device representing total device force as the sum of passive force from the VS resistance band and active force from the IPAM. The origami muscle assembly is in series but is assumed to be inextensible.
Figure 12: Characterization results. (a) Force profile tuning in the passive mode. (b) Rapid response of the EA clutch. (c) Force profile tuning in the active mode. (d) Peak device force with combined EA clutch and IPAM actuation. (e) Rapid response of the IPAM and the accurate prediction of the device force by the analytical model. (f) Dynamic force testing in the active mode and the accurate prediction of the dynamic device force by the analytical model. The shaded region represents ±1 standard deviation in all the sub-figures.
Figure 13: Device force profile optimization for object lowering and lifting. (a) Coupled device-musculoskeletal model. (b) Optimal force profile for object lowering. (c) Optimal force profile for object lifting.
Figure 14: State and weight estimation. (a) Overview of the automated weight adaptive force profile tuning algorithm. (b) Labeling of the training data for the state and the weight classifier. (c) Real-time state and weight classification in a representative subject. (d) Real-time state and weight classification accuracy across three subjects.
Figure 15: On-body Device Characterization. (a) Experimental protocol: Motion capture used to determine the trunk angle and the RoM, load cell used to measure the device force, and the wire draw encoder used to measure device elongation. (b) Force profile tuning in the passive mode that facilitates weight-adaptive lowering assistance. (c) Force profile tuning in the active mode that facilitates weight-adaptive lifting assistance. (d) Force vs device elongation in the active mode. (e) Trunk angle vs device elongation in the active mode. (f) Increasing energy added to the human body over a flexion-extension cycle with the increasing active force level.
Figure 16: (a) Experimental setup and protocol for (a) the symmetric lifting/lowering task. (b) the asymmetric lifting/lowering task.
Figure 17: Evaluation of back extensor and trunk flexor activity during symmetric object lifting and lowering. (a) Effect on the back extensors during weightlifting: The peak normalized root mean squared (RMS) EMG of ESL (mean of left and right) for individual subjects during weightlifting without and with the device. (b) Effect on the trunk flexors during weightlifting: The peak normalized RMS EMG of RA (mean of left and right) for individual subjects during weightlifting without and with the device. (c) Effect on the back extensors during weight lowering: The peak normalized RMS EMG of ESL (mean of left and right) for individual subjects during weight lowering without and with the device. (d) Effect on the trunk flexors during weight lowering: The peak normalized RMS EMG of RA (mean of left and right) for individual subjects during weight lowering without and with the device.
Figure 18: Evaluation of back extensor and trunk flexor activity during asymmetric object lifting and lowering (a) Reduction in the peak normalized RMS EMG of left ESL, right ESL, left EO, and right EO during asymmetric weight lifting in a single subject with the device. (b) Reduction in the peak normalized RMS EMG of left ESL, right ESL, left EO, and right EO during asymmetric weight lowering in a single subject with the device.
Older adults are particularly susceptible to falls following perturbations during standing, such as forward loss of balance. Back support devices that assist trunk extension may help mitigate fall risk by preventing excessive trunk flexion. Previous studies have investigated heavy back support devices; however, these systems often introduced adverse effects on stability due to their added mass, which shifted the body's natural center of mass unfavorably. In contrast, lightweight passive devices have shown limited benefits, as they can generate only modest assistive forces during the relatively small trunk flexion associated with forward balance loss. In this study, we evaluated the effects of a lightweight semi-active soft back support device on postural stability following standing perturbations. Our device combines an active element (a pneumatic artificial muscle) in parallel with a passive elastic band. The active element rapidly provides assistive force following a perturbation, overcoming the limitations of passive devices. Experiments conducted with five healthy individuals demonstrated that the semi-active device significantly reduced whole-body angular momentum and increased the margin of stability, indicating improved balance recovery performance. These results highlight the promise of semi-active soft wearable robots as an effective and lightweight strategy for fall prevention during standing perturbations.
Rohan Khatavkar, Jiefeng Sun, Hyunglae Lee
The authors are with the School for Engineering of Matter, Transport and Energy, Tempe, AZ 85287, USA.
The effectiveness of a soft wearable back-support device in enhancing postural stability was investigated under trip-like perturbations using two experimental paradigms: perturbed standing and perturbed walking. Healthy subjects completed trials under three different back-support conditions: no device, device worn with low stiffness, and device activated with high stiffness. Whole-body stability was quantified using the minimum Margin of Stability (MOS) at the point of maximal instability. Results demonstrated increased MOS during device use, indicating enhanced postural stability. In standing, MOS increased significantly with device stiffness, whereas in walking, both device conditions improved MOS relative to no device but did not differ significantly from each other. These findings highlight the potential of soft wearable back-support devices with adjustable stiffness to improve reactive balance control against external perturbations, with important implications for fall prevention. Future research should explore personalized stiffness optimization and evaluate efficacy in populations at elevated risk of falls.
Yuanhao Chen, Rohan Khatavkar, Soubhagya Nayak +2
School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, USA
Portable pneumatic systems for 2 degree-of-freedom (DOF) soft shoulder exosuits remain underexplored, and face fundamental trade-offs between torque output and dynamic response that are further compounded by the need for multiple actuators to support complex shoulder movement. This work addresses these constraints through a volume-optimized spindle-shaped angled actuator (SSAA) geometry: by reducing actuator volume by 35.7% (357mL vs. 555mL), the SSAA maintains 94.2% of output torque while achieving 35.2% faster dynamic response compared to uniform cylindrical designs. Building on the SSAA, we develop a curved abduction actuator (CAA) based on the SSAA geometry and a horizontal adduction actuator (HAA) based on the pouch motor principle, integrating both into a dual-DOF textile-based shoulder exosuit (390 g). The exosuit delivers multi-modal assistance spanning shoulder abduction, flexion, and horizontal adduction, depending on the actuation. User studies with 10 healthy participants reveal that the exosuit substantially reduces electromyographic (EMG) activity across both shoulder abduction and flexion tasks. For abduction with HAA only, the exosuit achieved up to 59% muscle activity reduction across seven muscles. For flexion, both the single-actuator configuration (HAA only) and the dual-actuator configuration (HAA,+,CAA) reduced EMG activity by up to 63.7% compared to no assistance. However, the incremental benefit of adding the CAA to existing HAA support was limited in healthy users during flexion, with statistically significant additional reductions observed only in pectoralis major. These experimental findings characterize actuator contributions in healthy users and provide design guidance for multi-DOF exosuit systems.
The authors are with the Institute of Mechanical Intelligence and Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna (SSSA), 56127 Pisa, Italy