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Tibial stress fractures are a common overuse injury resulting from the accumulation of bone microdamage due to repeated loading. Researchers and wearable device developers have sought to understand or predict stress fracture risks, and other injury risks, by monitoring the ground reaction force (GRF, the force between the foot and ground), or GRF correlates (e.g., tibial shock) captured via wearable sensors. Increases in GRF metrics are typically assumed to reflect increases in loading on internal biological structures (e.g., bones). The purpose of this study was to evaluate this assumption for running by testing if increases in GRF metrics were strongly correlated with increases in tibial compression force over a range of speeds and slopes. Ten healthy individuals performed running trials while we collected GRFs and kinematics. We assessed if commonly-used vertical GRF metrics (impact peak, loading rate, active peak, impulse) were strongly correlated with tibial load metrics (peak force, impulse). On average, increases in GRF metrics were not strongly correlated with increases in tibial load metrics. For instance, correlating GRF impact peak and loading rate with peak tibial load resulted in r = -0.29±0.37 and r = -0.20±0.35 (inter-subject mean and standard deviation), respectively. We observed high inter-subject variability in correlations, though most coefficients were negligible, weak or moderate. Seventy-six of the 80 subject-specific correlation coefficients computed indicated that higher GRF metrics were not strongly correlated with higher tibial forces. These results demonstrate that commonly-used GRF metrics can mislead our understanding of loading on internal structures, such as the tibia. Increases in GRF metrics should not be assumed to be an indicator of increases in tibial bone load or overuse injury risk during running. This has important implications for sports, wearable devices, and research on running-related injuries, affecting >50 scientific publications per year from 2015-2017.

(1) Background: Low back disorders are a leading cause of missed work and physical disability in manual material handling due to repetitive lumbar loading and overexertion. Ergonomic assessments are often performed to understand and mitigate the risk of musculoskeletal overexertion injuries. Wearable sensor solutions for monitoring low back loading have the potential to improve the quality, quantity, and efficiency of ergonomic assessments and to expand opportunities for the personalized, continuous monitoring of overexertion injury risk. However, existing wearable solutions using a single inertial measurement unit (IMU) are limited in how accurately they can estimate back loading when objects of varying mass are handled, and alternative solutions in the scientific literature require so many distributed sensors that they are impractical for widespread workplace implementation. We therefore explored new ways to accurately monitor low back loading using a small number of wearable sensors. (2) Methods: We synchronously collected data from laboratory instrumentation and wearable sensors to analyze 10 individuals each performing about 400 different material handling tasks. We explored dozens of candidate solutions that used IMUs on various body locations and/or pressure insoles. (3) Results: We found that the two key sensors for accurately monitoring low back loading are a trunk IMU and pressure insoles. Using signals from these two sensors together with a Gradient Boosted Decision Tree algorithm has the potential to provide a practical (relatively few sensors), accurate (up to r2 = 0.89), and automated way (using wearables) to monitor time series lumbar moments across a broad range of material handling tasks. The trunk IMU could be replaced by thigh IMUs, or a pelvis IMU, without sacrificing much accuracy, but there was no practical substitute for the pressure insoles. The key to realizing accurate lumbar load estimates with this approach in the real world will be optimizing force estimates from pressure insoles. (4) Conclusions: Here, we present a promising wearable solution for the practical, automated, and accurate monitoring of low back loading during manual material handling.

Recent literature emphasizes the importance of comfort in the design of exosuits and other assistive devices that physically augment humans; however, there is little quantitative data to aid designers in determining what level of force makes users uncomfortable. To help close this knowledge gap, we characterized human comfort limits when applying forces to the shoulders, thigh and shank. Our objectives were: (i) characterize the comfort limits for multiple healthy participants, (ii) characterize comfort limits across days, and (iii) determine if comfort limits change when forces are applied at higher vs. lower rates. We performed an experiment (N = 10) to quantify maximum tolerable force pulling down on the shoulders, and axially along the thigh and shank; we termed this force the comfort limit. We applied a series of forces of increasing magnitude, using a robotic actuator, to soft sleeves around their thigh and shank, and to a harness on their shoulders. Participants were instructed to press an off-switch, immediately removing the force, when they felt uncomfortable such that they did not want to feel a higher level of force. On average, participants exhibited comfort limits of ~0.9-1.3 times body weight on each segment: 621±245 N (shoulders), 867±296 N (thigh), 702±220 N (shank), which were above force levels applied by exosuits in prior literature. However, individual participant comfort limits varied greatly (~250-1200 N). Average comfort limits increased over multiple days (p<3e-5), as users habituated, from ~550-700 N on the first day to ~650-950 N on the fourth. Specifically, comfort limits increased 20%, 35% and 22% for the shoulders, thigh and shank, respectively. Finally, participants generally tolerated higher force when it was applied more rapidly. These results provide initial benchmarks for exosuit designers and end-users, and pave the way for exploring comfort limits over larger time scales, within larger samples and in different populations.

Knee exoskeletons have been developed to assist, stabilize, or improve human movement or recovery. However, exoskeleton designers must implement transparency (i.e., get out of the way) modes during the swing phase of locomotor tasks to avoid impeding movement. The problem is that it is not understood how sensitive people are to small knee torques or what level of knee impedance is acceptable (sufficiently transparent) during swing phase. Here, we (i) characterized the biomechanical consequences of knee stiffness and damping during swing, and (ii) leveraged user perceptions of being impeded and toe clearance to define transparency thresholds, below which the participants were sufficiently unimpeded during the swing phase of gait. We conducted a series of human subject experiments that involved walking and stair ascent/descent while wearing a modified knee brace with five stiffness values ranging from 0 to 4 Nm/rad and five damping values ranging from 0 to 0.77 Nm/rad/s. We measured changes to lower limb kinematics, knee flexor muscle activity, and participants’ perception of being impeded during swing. Kinematics, muscle activity, and perceived impedance all changed in response to added stiffness and damping. For stiffness, we found the median transparency thresholds for walking and stairs to be 1.76 Nm/rad and 2.95 Nm/rad, respectively, which corresponds to peak knee moments during swing of around 2.3 and 5 Nm. For damping, we found the median transparency threshold for walking and stairs to be about the same, 0.29 Nm/rad/s, which corresponds to peak knee moments during swing of around 2.3 Nm. These values provide useful benchmarks for defining quantitative design requirements for knee exoskeletons intended for locomotor activities.

Background Wearable assistive devices have demonstrated the potential to improve mobility outcomes for individuals with disabilities, and to augment healthy human performance; however, these benefits depend on how effectively power is transmitted from the device to the human user. Quantifying and understanding this power transmission is challenging due to complex human-device interface dynamics that occur as biological tissues and physical interface materials deform and displace under load, absorbing and returning power. Methods Here we introduce a new methodology for quickly estimating interface power dynamics during movement tasks using common motion capture and force measurements, and then apply this method to quantify how a soft robotic ankle exosuit interacts with and transfers power to the human body during walking. We partition exosuit end-effector power (i.e., power output from the device) into power that augments ankle plantarflexion (termed augmentation power) vs. power that goes into deformation and motion of interface materials and underlying soft tissues (termed interface power). Results We provide empirical evidence of how human-exosuit interfaces absorb and return energy, reshaping exosuit-to-human power flow and resulting in three key consequences: (i) During exosuit loading (as applied forces increased), about 55% of exosuit end-effector power was absorbed into the interfaces. (ii) However, during subsequent exosuit unloading (as applied forces decreased) most of the absorbed interface power was returned viscoelastically. Consequently, the majority (about 75%) of exosuit end-effector work over each stride contributed to augmenting ankle plantarflexion. (iii) Ankle augmentation power (and work) was delayed relative to exosuit end-effector power, due to these interface energy absorption and return dynamics. Conclusions Our findings elucidate the complexities of human-exosuit interface dynamics during transmission of power from assistive devices to the human body, and provide insight into improving the design and control of wearable robots. We conclude that in order to optimize the performance of wearable assistive devices it is important, throughout design and evaluation phases, to account for human-device interface dynamics that affect power transmission and thus human augmentation benefits.

Walking on sloped surfaces is challenging for many lower limb prosthesis users, in part due to the limited ankle range of motion provided by typical prosthetic ankle-foot devices. Adding a toe joint could potentially benefit users by providing an additional degree of flexibility to adapt to sloped surfaces, but this remains untested. The objective of this study was to characterize the effect of a prosthesis with an articulating toe joint on the preferences and gait biomechanics of individuals with unilateral below-knee limb loss walking on slopes. Nine active prosthesis users walked on an instrumented treadmill at a +5° incline and -5° decline while wearing an experimental foot prosthesis in two configurations: a Flexible toe joint and a Locked-out toe joint. Three participants preferred the Flexible toe joint over the Locked-out toe joint for incline and decline walking. Eight of nine participants went on to participate in a biomechanical data collection. The Flexible toe joint decreased prosthesis Push-off work by 2 Joules during both incline (p = 0.008; g = -0.63) and decline (p = 0.008; g = -0.65) walking. During incline walking, prosthetic limb knee flexion at toe-off was 3° greater in the Flexible configuration compared to the Locked (p = 0.008; g = 0.42). Overall, these results indicate that adding a toe joint to a passive foot prosthesis has relatively small effects on joint kinematics and kinetics during sloped walking. This study is part of a larger body of work that also assessed the impact of a prosthetic toe joint for level and uneven terrain walking and stair ascent/descent. Collectively, toe joints do not appear to substantially or consistently alter lower limb mechanics for active unilateral below-knee prosthesis users. Our findings also demonstrate that user preference for passive prosthetic technology may be both subject-specific and task-specific. Future work could investigate the inter-individual preferences and potential benefits of a prosthetic toe joint for lower-mobility individuals.

Ultrasound imaging is increasingly used with motion and force data to quantify tendon dynamics during human movement. Frequently, tendon dynamics are estimated indirectly from muscle fascicle kinematics (by subtracting muscle from muscle-tendon unit length), but there is mounting evidence that this Indirect approach yields implausible tendon work loops. Since tendons are passive viscoelastic structures, when they undergo a loading-unloading cycle they must exhibit a negative work loop (i.e., perform net negative work). However, prior studies using this Indirect approach report large positive work loops, often estimating that tendons return 2-5 J of elastic energy for every 1 J of energy stored. More direct ultrasound estimates of tendon kinematics have emerged that quantify tendon elongations by tracking either the muscle-tendon junction or localized tendon tissue. However, it is unclear if these yield more plausible estimates of tendon dynamics. Our objective was to compute tendon work loops and hysteresis losses using these two Direct tendon kinematics estimates during human walking. We found that Direct estimates generally resulted in negative work loops, with average tendon hysteresis losses of 2-11% at 1.25 m/s and 33-49% at 0.75 m/s (N = 8), alluding to 0.51-0.98 J of tendon energy returned for every 1 J stored. We interpret this finding to suggest that Direct approaches provide more plausible estimates than the Indirect approach, and may be preferable for understanding tendon energy storage and return. However, the Direct approaches did exhibit speed-dependent trends that are not consistent with isolated, in vitro tendon hysteresis losses of about 5-10%. These trends suggest that Direct estimates also contain some level of error, albeit much smaller than Indirect estimates. Overall, this study serves to highlight the complexity and difficulty of estimating tendon dynamics non-invasively, and the care that must be taken to interpret biological function from current ultrasound-based estimates.

Low back disorders (LBDs) are a leading occupational health issue. Wearable sensors, such as inertial measurement units (IMUs) and/or pressure insoles, could automate and enhance the ergonomic assessment of LBD risks during material handling. However, much remains unknown about which sensor signals to use and how accurately sensors can estimate injury risk. The objective of this study was to address two open questions: (1) How accurately can we estimate LBD risk when combining trunk motion and under-the-foot force data (simulating a trunk IMU and pressure insoles used together)? (2) How much greater is this risk assessment accuracy than using only trunk motion (simulating a trunk IMU alone)? We developed a data-driven simulation using randomized lifting tasks, machine learning algorithms, and a validated ergonomic assessment tool. We found that trunk motion-based estimates of LBD risk were not strongly correlated (r range: 0.20–0.56) with ground truth LBD risk, but adding under-the-foot force data yielded strongly correlated LBD risk estimates (r range: 0.93–0.98). These results raise questions about the adequacy of a single IMU for LBD risk assessment during material handling but suggest that combining an IMU on the trunk and pressure insoles with trained algorithms may be able to accurately assess risks.

Biomechanics principally stems from two disciplines, mechanics and biology. However, both the application and language of the mechanical constructs are not always adhered to when applied to biological systems, which can lead to errors and misunderstandings within the scientific literature. Here we address three topics that seem to be common points of confusion and misconception, with a specific focus on sports biomechanics applications: (1) joint reaction forces as they pertain to loads actually experienced by biological joints; (2) the partitioning of scalar quantities into directional components; and (3) weight and gravity alteration. For each topic, we discuss how mechanical concepts have been commonly misapplied in peer-reviewed publications, the consequences of those misapplications, and how biomechanics, exercise science, and other related disciplines can collectively benefit by more carefully adhering to and applying concepts of classical mechanics.

To descend a flight of stairs, would you rather walk or fall? Falling seems to have some obvious disadvantages such as the risk of pain or injury. But the preferred strategy of walking also entails a cost for the use of active muscles to perform negative work. The amount and distribution of work a person chooses to perform may, therefore, reflect a subjective valuation of the trade-offs between active muscle effort and other costs, such as pain. Here we use a simple jump landing experiment to quantify the work humans prefer to perform to dissipate the energy of landing. We found that healthy normal subjects (N = 8) preferred a strategy that involved performing 37% more negative work than minimally necessary (P<0.001) across a range of landing heights. This then required additional positive work to return to standing rest posture, highlighting the cost of this preference. Subjects were also able to modulate the amount of landing work, and its distribution between active and passive tissues. When instructed to land softly, they performed 76% more work than necessary (P<0.001), with a higher proportion from active muscles (89% vs. 84%, P<0.001). Stiff-legged landings, performed by one subject for demonstration, exhibited close to the minimum of work, with more of it performed passively through soft tissue deformations (at least 30% in stiff landings vs. 16% preferred). During jump landings, humans appear not to minimize muscle work, but instead choose to perform a consistent amount of extra work, presumably to avoid other subjective costs. The degree to which work is not minimized may indirectly quantify the relative valuation of costs that are otherwise difficult to measure.