Work-related musculoskeletal disorders (WMSD) account for about 25% of the total workers’ compensation cost in the US (Liberty-Mutual 2017), approximately 40% (95,690 out of 247,620) of which are low-back injuries (BLS 2021). Exoskeletons, wearable devices intended to augment, enable, or enhance human physical capabilities, are populating workplaces as a potential means to prevent musculoskeletal injuries. Low-back exoskeletons are designed to reduce mechanical loading on the low-back while preserving the functionality and mobility of workers during tasks involving bending and lifting.

Exoskeletons can be categorised as active or passive (Wesslén 2018), and passive low-back exoskeletons have been increasingly adopted to assist manual materials handling tasks due to their relatively low cost and ease of use (MacDougall 2014). Muscle activity and kinematics measurements of the lumbar region are commonly acquired in demonstrating the efficacy or assessing the benefits of exoskeletons (Zheng et al. 2021). Previous studies have examined the effects of passive low-back exoskeletons using surface electromyography (EMG) and reported reduced back muscle activity as a result of wearing the exoskeletons (Abdoli-E, Agnew, and Stevenson 2006; Abdoli-e and Stevenson 2008; Alemi et al. 2019; Bosch et al. 2016; de Looze et al. 2016; Graham, Agnew, and Stevenson 2009; Hwang et al. 2021; Lotz et al. 2009; Madinei et al. 2020; Ulrey and Fathallah 2013; von Glinski et al. 2019). However, a reduction in back muscle activity alone does not necessarily imply a reduction in spine loading, especially when a significant lumbar kinematics change also accompanies the use of an exoskeleton. This is due to the flexion-relaxation effect (Koopman et al. 2019; Ulrey and Fathallah 2013) that occurs when in substantial trunk bending, passive tissues are stretched far enough to generate a major portion of the required extension moment such that back muscles are de-activated (Floyd and Silver 1955). Furthermore, it is often difficult to weigh the trade-offs between observed benefits and costs such as reduced muscle activation versus mechanical load transfer. Therefore, a comprehensive understanding of biomechanical risks and benefits associated with the use of exoskeletons necessitates a deeper, model-based analysis of the underlying mechanical loads and in particular the loads experienced by the lumbar spine.

Conventional joint kinematics and dynamics measurements and analyses often employ optical motion capture systems and force plates. However, optical motion capture systems require marker visibility and thus have limited applicability in assessing human-exoskeleton interactions since the exoskeletons may obstruct surface markers over areas of interest such as the trunk and pelvis. In some studies, reflective markers were placed on the exoskeleton to approximate the human motion (Weston et al. 2018), which may jeopardise the quality of the data and validity of the findings. Some other studies used cluster markers, attached to the pelvis using bandages and connecting rods to estimate participants’ lumbar motion (Koopman et al. 2019, 2020). Such a strategy, nevertheless, may interfere with or alter subjects’ performance and significantly bias the measurements. Inertial-magnetic sensors can provide a cost-effective alternative with no visibility restriction, but their applications have been hampered by issues such as performance interference and data artefact due to their size and the way they are attached to the body (Yin et al. 2021). For example, once an inertial measurement unit (IMU) wearable sensor is attached to a body region or segment, it would inhibit the addition and proper use of a wearable assistive device such as exoskeleton.

A new technology of wireless, miniaturised, skin-mounted, multimode sensors, in particular BioStamp nPoint (MC10, Inc., MA, USA), has shown its great promise in addressing the above-stated challenges. Its flexible design allows for direct body-conforming adhesion to the skin surfaces. This sensor integrates an accelerometer, gyroscope, and biopotential measurement unit and can be controlled with a tablet through a Bluetooth connection without any additional hardware, thus supporting potential ambulatory data recording for both laboratory and field studies (Sen-Gupta et al. 2019). Several recent studies have demonstrated the viability of these flexible sensors for measuring simple gait parameters (e.g. stride numbers, stride time) (Moon et al. 2017) and body kinematics (Jeong et al. 2021; Yin et al. 2021), and assessing physical activity or stress in clinical and field studies (Sen-Gupta et al. 2019; Zahabi et al. 2022). Most recently, a preliminary study further explored the applicatin of flexible sensors in acquiring lifting kinematics and kinetics estimates and discerning the effects of a back exoskeleton (Yin et al. 2022).

Therefore, the objective of this study was twofold. The first objective was to develop a flexible sensor-based system for measuring the torso, low-back, and lower-extremity kinematics and low-back dynamics and muscle activity and establish its concurrent validity by comparing the measures with those from a simultaneously deployed conventional optical motion capture system. The second objective was to demonstrate the utility of the proposed flexible sensor-based system by a biomechanical evaluation of passive low-back exoskeleton use in lifting and pave the way for future field or on-site studies of the human- exoskeleton interactions.

This study established a flexible sensor-based system capable of measuring the lumbar, hip, knee, and ankle joint kinematics and L5S1 flexion moment during a symmetric lifting task, and investigated its validity by comparing to a simultaneously deployed conventional marker-based motion capture system. Results indicated that the proposed new system yielded excellent agreement with the marker-based system (r>0.90) (Poitras et al. 2019), whereas the RMSEs appeared to be more variable across joints.

With regard to the joint angle estimation, as defined by Cuesta-Vargas, Galán-Mercant, and Williams (2010) and Poitras et al. (2019), a RMSE of <5° is considered as excellent and between 5° and 10° as good. Based on that criterion, the proposed method achieved good to excellent agreement with the marker-based approach for the hip, knee, and ankle joints. However, the results for the lumbar spine showed poorer agreement than the other joints. The lumbar spine joint also had the lowest r-value compared with the other joints. Larger RMSE and lower correlation coefficient in estimating pelvic orientation when subjects performed tasks involving substantial pelvic movement (e.g. sit-to-stand transfers, with RMSE = 8.9° and r = 0.92) have been previously reported (Bolink et al. 2016). This may have been attributable to the excessive soft tissue artefact associated with the markers attached to the pelvic region when the subjects were in a low, squatting position. Additionally, when the subjects attained the lowest squatting position, markers on the left and right ASIS might have been completely obstructed by the abdominal pannus and thigh and substantial gap-filling required in motion tracking could have introduced more error. In contrast, the skin-integrated flexible sensors attached to the sacrum and T12 spinal landmarks incur less soft tissue artefact and are immune to the visual blockage problem. The joint kinematics profiles of all subjects were consistent (with the ones in Figure 4) in showing a greater discrepancy in the lumbar flexion profiles compared with other joints, especially during 40–60% time of completion when the subject reached the lowest position. Of note is that the marker-based measurements here were not used as the ‘gold standard’ but rather a reference line. The accuracy of the proposed sensor-based method in kinematic measurement would have to be assessed by a comparison with real ‘ground truth’ acquired by in vivo dynamic X-ray imaging as has been done for marker-based methods (Li et al. 2012). The estimation of L5S1 flexion moment using the proposed sensor-based system also generated poorer agreement with the marker-based method than the estimated hip, knee, and ankle kinematics. This could be due to the accumulation of errors in calculating hip, knee, and ankle joint kinematics because, in the bottom-up estimation method, L5S1 flexion moment was calculated by iterating the motions of lower segments (Faber, Kingma, and van Dieën 2010).

The sensor calibration procedure could have been another potential source of error. The anatomical standing position was used to calibrate the sensors’ coordinate systems in which the joint angles were presumed to be zero, as defined by the OpenSim model. The hip abduction/adduction movement was used to calibrate the sensor attached to lower extremities, in which the knee joint was assumed to be motionless and the hip joint was assumed to undergo abduction/adduction only. Neither however could be precisely controlled, and offsets would thus be created if the subject did not execute the predefined position or motion correctly. Additionally, the inaccuracy in attaching sensors to the sacrum and T12 could also have contributed to deviation of sensor calibration because the sensor’s Z-axis was assumed to be in the sagittal plane of sacrum and T12.

The proposed method achieved overall a comparable level of accuracy in simulating hip, knee, and ankle joint kinematics (Al-Amri et al. 2018; Lebel et al. 2017; McGrath, Fineman, and Stirling 2018; Robert-Lachaine et al. 2017; Seel, Raisch, and Schauer 2014; Yin et al. 2021). Some studies obtained better agreement with marker-based measurement for pelvic orientation or lumbar spine angles (Bauer et al. 2015; Godwin, Agnew, and Stevenson 2009; Mjosund et al. 2017; Robert-Lachaine et al. 2017); however, the reported performances were limited to quasi-static motions or motions that did not involve substantial pelvic movement. Bolink et al. (2016) reported larger RMSE and smaller r in estimating pelvic orientation for sit-to-stand transfers than for gait, which might be attributed to the inaccuracy of marker-based simulation as discussed above. The performance of the proposed method for L5S1 flexion moment estimation was comparable or better than those reported in previous studies using a video-based system (Mehrizi et al. 2019), the orientation-based method (Faber, Kingma, and van Dieën 2010), and inertial-magnetic sensors (Faber et al. 2020; Koopman et al. 2018). Although the current study relied on force plates to capture the ground reaction forces and moments, the proposed system could be integrated with instrumented shoes or pressure insoles to enable ambulatory measurement of L5S1 joint moments. Most relevant studies that adopted inertial-magnetic sensors had limited applications in naturalistic settings due to the sensors’ size and the way they are attached to the body (Al-Amri et al. 2018; Bauer et al. 2015; Faber et al. 2020; Godwin, Agnew, and Stevenson 2009; Koopman et al. 2018; Lebel et al. 2017; McGrath, Fineman, and Stirling 2018; Mjosund et al. 2017; Robert-Lachaine et al. 2017; Seel, Raisch, and Schauer 2014). By contrast, flexible sensor-based studies have so far not identified any issues on sensor wearability, subject comfort, or performance interference (Moon et al. 2017; Sen-Gupta et al. 2019), encouraging further, more assertive exploration of this technology in field evaluation of human-exoskeleton or human-wearable interactions.

The effects of a passive low-back exoskeleton on users’ lower body joint kinematics, L5S1 flexion moments, and back muscles were investigated using our proposed flexible sensor-based system. The observed increased peak lumbar flexion and decreased peak L5S1 flexion moments due to exoskeleton use were consistent with the prior research (Bosch et al. 2016; Hwang et al. 2021; Koopman et al. 2020; Koopman et al. 2019) on the same device. However, our results showed larger variability in the increase of lumbar flexion (by 1.4–27.6°) and the reduction of L5S1 flexion moments (by 15.84–53.05 N·m) which could be explained by the fact that the present study did not control the relative lifting positions such that each subject might perform a different percentage of the lumbar flexion range of motion. Some studies, however, reported reduced or constant lumbar flexion when subjects wore exoskeletons (Picchiotti et al. 2019; Ulrey and Fathallah 2013), which might be due to subtle differences in devices or task constraints.

To our knowledge, this was the first study that reported the changes in lumbar, hip, knee, and ankle flexion, and L5S1 flexion moment simultaneously. Apart from significantly increased lumbar flexion and reduced L5S1 flexion moment, our study identified significantly decreased hip flexion and decreased knee flexion (p=0.067, marginally significant). Aberrations in thigh segments were also found by von Glinski et al. (2019), in which significantly increased quadriceps femoris activity when users wore a low-back exoskeleton was reported. Similar mechanical load shifts or transfers were also identified in the evaluation of other passive exoskeletons (e.g. Weston et al. (2018)). Thus, the impacts of low-back exoskeletons on biomechanics of the lower extremities may warrant more investigative attention in future studies.

Significant reductions of back muscle EMG amplitude (mean reduction: 15.9% for LES, 12.8% for TES) were detected. The amount of reduction indicated smaller effects of the exoskeleton on reducing back muscle activity during lifting in comparison with other studies or other devices that showed mean reductions of more than 30% (Abdoli-E, Agnew, and Stevenson 2006; Abdoli-e and Stevenson 2008; Alemi et al. 2019). However, it should be noted that, due to the flexion-relaxation phenomenon, EMG reduction did not necessarily indicate back muscle load reduction (Koopman et al. 2019; Ulrey and Fathallah 2013). Since Abdoli-E, Agnew, and Stevenson (2006) and Alemi et al. (2019) did not report either lumbar flexion or L5S1 loads, it is unclear whether the exoskeletons being investigated were effective in reducing low-back load, which underscores the importance of assessing the L5S1 joint loading when evaluating the efficacy of low-back exoskeletons.

Several limitations of this study are acknowledged. First, because the BioStamp nPoint sensors are not yet capable of acquiring the global orientation information, the proposed system presently can only measure joint motions in a single plane (e.g. the sagittal plan in this study). A new set of algorithms and calibration procedures would be needed to derive 3D joint kinematics estimates based on the flexible sensors. Secondly, the proposed system configuration, including the number of sensors and their placement strategy, was designed for estimating lumbar and lower limb dynamics for the evaluation of low-back exoskeletons. The configuration would need to be modified and optimised for investigations focussed more on the upper body and upper extremities. Lastly, while our aim was to develop a completely laboratory-free system, the current approach still required the involvement of force plates, which are laboratory-bound. Future endeavours will integrate the proposed system with instrumented shoes or pressure insoles to enable completely ambulatory biomechanical measurement for evaluating human-exoskeleton interactions in the field.

In conclusion, this study explored a flexible sensor-based ambulatory system for acquiring biomechanical data during human-exoskeleton interactions. Results demonstrated that the proposed system was able to generate biodynamic measures comparable to marker-based measurements and discern the effects of a passive low-back exoskeleton on body kinematics, L5S1 moment, and back muscle activity.