Stoop Lifting Research Articles

Intervertebral disc deformation in the lower lumbar spine during object lifting measured in vivo using indwelling bone pins

Object lifting is often categorized into squat and stoop techniques, with the former believed to protect the back by maintaining a neutral spine, and the latter considered harmful due to spinal flexion. Despite the widespread promotion of these beliefs, there is no evidence to support such dichotomy, as spinal flexion is not conclusively linked to low back pain. This study aimed to investigate intervertebral disc deformation in the lower lumbar spine during squat and stoop lifting using indwelling bone pins. Five healthy males underwent insertion of Kirschner wires into the L3, L4, and L5 spinous processes, followed by biomechanical data collection using magnetic and optical tracking systems during upright standing, isolated flexion/extension, and object lifting with both squat and stoop techniques. Except for one subject, stoop lifting resulted in up to 90 % greater disc wedging compared to squat lifting, with a significant difference at L4/L5 (p = 0.042). The anterior annulus fibrosus experienced 10 % to 40 % more compression during stoop lifting, but no significant differences were found in posterior annulus fibrosus expansion between techniques. Lever arms were about 35 % longer during stoop compared to squat lifting. These results indicate that even though stoop lifting generally led to greater disc deformation, significant deformation was also observed during squat lifting, challenging the notion of maintaining a neutral spine with this technique. Moreover, the considerable variability observed among participants raises concerns about the suitability of current one-size-fits-all lifting guidelines.

The effect of a soft active back support exosuit on trunk motion and thoracolumbar spine loading during squat and stoop lifts

Back support exosuits aim to reduce tissue demands and thereby risk of injury and pain. However, biomechanical analyses of soft active exosuit designs have been limited. The objective of this study was to evaluate the effect of a soft active back support exosuit on trunk motion and thoracolumbar spine loading in participants performing stoop and squat lifts of 6 and 10 kg crates, using participant-specific musculoskeletal models. The exosuit did not change overall trunk motion but affected lumbo-pelvic motion slightly, and reduced peak compressive and shear vertebral loads at some levels, although shear increased slightly at others. This study indicates that soft active exosuits have limited kinematic effects during lifting, and can reduce spinal loading depending on the vertebral level. These results support the hypothesis that a soft exosuit can assist without limiting trunk movement or negatively impacting skeletal loading and have implications for future design and ergonomic intervention efforts.

Musculoskeletal models determine the effect of a soft active exosuit on muscle activations and forces during lifting and lowering tasks

Exosuits have the potential to mitigate musculoskeletal stress and prevent back injuries during industrial tasks. This study aimed to 1) validate the implementation of a soft active exosuit into a musculoskeletal model of the spine by comparing model predicted muscle activations versus corresponding surface EMG measurements, and 2) evaluate the effect of the exosuit on peak back and hip muscle forces. Fourteen healthy participants performed squat and stoop lift and lower tasks with boxes of 6 and 10 kg, with and without wearing a 2.7 kg soft active exosuit. Participant-specific musculoskeletal models, which included the exosuit, were created in OpenSim. Model validation focused on the back and hip extensors, where temporal agreement between EMG and model estimated muscle activity was generally strong to excellent (average cross-correlation coefficients ranging from 0.84 to 0.98). Root mean square errors of muscle activity (0.05–0.10) were similar with and without the exosuit, and compared well to prior model validation studies without the exosuit (average root mean square errors ranging from 0.05 to 0.19). In terms of performance, the exosuit reduced the estimated peak erector spinae forces during lifting and lowering phases across all lifting tasks but reduced peak hip extensor muscles forces only in a squat lift task of 10 kg. These reductions in total peak muscle forces were approximately 1.7–4.2 times greater than the corresponding exosuit assistance force, which were 146 ± 19 N and 102 ± 14 N at the times of peak erector spinae forces in lifting and lowering, respectively. Overall, the results support the hypothesis that exosuits reduce soft tissue loading, and thereby potentially reduce fatigue and injury risk during manual materials handling tasks. Incorporating exosuits into musculoskeletal models is a valid approach to understand the impact of exosuit assistance on muscle activity and forces.

Loading Recognition for Lumbar Exoskeleton Based on Multi-Channel Surface Electromyography From Low Back Muscles.

Lumbar exoskeleton is an assistive robot, which can reduce the risk of injury and pain in low back muscles when lifting heavy objects. An important challenge it faces involves enhancing assistance with minimal muscle energy consumption. One of the viable solutions is to adjust the force or torque of assistance in response to changes in the load on the low back muscles. It requires accurate loading recognition, which has yet to yield satisfactory outcomes due to the limitations of available measurement tools and load classification methods. This study aimed to precisely identify muscle loading using a multi-channel surface electromyographic (sEMG) electrode array on the low back muscles, combined with a participant-specific load classification method. Ten healthy participants performed a stoop lifting task with objects of varying weights, while sEMG data was collected from the low back muscles using a 3x7 electrode array. Nineteen time segments of the lifting phase were identified, and time-domain sEMG features were extracted from each segment. Participant-specific classifiers were built using four classification algorithms to determine the object weight in each time segment, and the classification performance was evaluated using a 5-fold cross-validation method. The artificial neural network classifier achieved an impressive accuracy of up to 96%, consistently improving as the lifting phase progressed, peaking towards the end of the lifting movement. This study successfully achieves accurate recognition of load on low back muscles during the object lifting task. The obtained results hold significant potential in effectively reducing muscle energy consumption when wearing a lumbar exoskeleton.

In-Field Training of a Passive Back Exoskeleton Changes the Biomechanics of Logistic Workers

OCCUPATIONAL APPLICATIONS Occupational exoskeletons receive rising interest in industry as these devices diminish the biomechanical load during manual materials handling. Still, we have limited knowledge when it comes to in-field use. This gap often contributes to failure in the implementation of exoskeleton in industry. In this study, we investigated how a training protocol consisting of in-field use of a passive back exoskeleton affected the biomechanics of logistic workers. More specifically, we focused on how the variation of the muscular and kinematic patterns of the user was altered after exoskeleton training. We found that training had a positive effect on exoskeleton use, as a relative decrease of 6–9% in peak back muscle activity was observed post-training. Additionally, training decreased knee flexion by 6°–16°, indicating a more stoop lifting technique. The findings point at the potential benefits of applying a training approach when implementing a back-supporting exoskeleton in logistics.

Comparing risk assessment methods for work-related musculoskeletal disorders with in vivo joint loads during manual materials handling

The validity of observational methods in ergonomics is still challenging research. Criterion validity in terms of concurrent validity is the most commonly studied. However, studies comparing observational methods with biomechanical values are rare. Thus, the aim of this study is to compare the Ovako Working Posture Analysing System (OWAS) and the Rapid Entire Body Assessment (REBA) with in vivo load measurements at hip, spine, and knee during stoop and squat lifting of 14 participants. The results reveal that OWAS and REBA action levels (AL) can distinguish between different in vivo load measurements during manual lifting. However, the results also reveal that the same OWAS- and REBA-AL do not necessarily provide equal mean values of in vivo load measurements. For example, resultant contact force in the vertebral body replacement for squat lifting ranged from 57% body weight (%BW) in OWAS-AL1 to 138%BW in OWAS-AL3 compared to 46%BW in REBA-AL0 and 173%BW in REBA-AL3. Furthermore, the results suggest that the performed squat lifting techniques had a higher risk for work-related musculoskeletal disorders than the performed stoop lifting techniques.

A Smart, Textile-Driven, Soft Exosuit for Spinal Assistance.

Work-related musculoskeletal disorders (WMSDs) are often caused by repetitive lifting, making them a significant concern in occupational health. Although wearable assist devices have become the norm for mitigating the risk of back pain, most spinal assist devices still possess a partially rigid structure that impacts the user's comfort and flexibility. This paper addresses this issue by presenting a smart textile-actuated spine assistance robotic exosuit (SARE), which can conform to the back seamlessly without impeding the user's movement and is incredibly lightweight. To detect strain on the spine and to control the smart textile automatically, a soft knitting sensor that utilizes fluid pressure as a sensing element is used. Based on the soft knitting hydraulic sensor, the robotic exosuit can also feature the ability of monitoring and rectifying human posture. The SARE is validated experimentally with human subjects (N = 4). Through wearing the SARE in stoop lifting, the peak electromyography (EMG) signals of the lumbar erector spinae are reduced by 22.8% ± 12 for lifting 5 kg weights and 27.1% ± 14 in empty-handed conditions. Moreover, the integrated EMG decreased by 34.7% ± 11.8 for lifting 5 kg weights and 36% ± 13.3 in empty-handed conditions. In summary, the artificial muscle wearable device represents an anatomical solution to reduce the risk of muscle strain, metabolic energy cost and back pain associated with repetitive lifting tasks.

Real-time lumbosacral joint loading estimation in exoskeleton-assisted lifting conditions via electromyography-driven musculoskeletal models

Lumbar joint compression forces have been linked to the development of chronic low back pain, which is specially present in occupational environments. Offline methodologies for lumbosacral joint compression force estimation are not commonly integrated in occupational or medical applications due to the highly time-consuming and complex post-processing procedures. Hence, applications such as real-time adjustment of assistive devices (i.e., back-support exoskeletons) for optimal modulation of compression forces remains unfeasible. Here, we present a real-time electromyography (EMG)-driven musculoskeletal model, capable of estimating accurate lumbosacral joint moments and plausible compression forces. Ten participants performed box-lifting tasks (5 and 15 kg) with and without the Laevo Flex back-support exoskeleton using squat and stoop lifting techniques. Lumbosacral kinematics and EMGs from abdominal and thoracolumbar muscles were used to drive, in real-time, subject-specific EMG-driven models, and estimate lumbosacral joint moments and compression forces. Real-time EMG-model derived moments showed high correlations (R2 = 0.76 - 0.83) and estimation errors below 30% with respect to reference inverse dynamic moments. Compared to unassisted lifting conditions, exoskeleton liftings showed mean lumbosacral joint moments and compression forces reductions of 11.9 - 18.7 Nm (6 - 12% of peak moment) and 300 - 450 N (5 - 10%), respectively. Our modelling framework was capable of estimating in real-time, valid lumbosacral joint moments and compression forces in line with in vivo experimental data, as well as detecting the biomechanical effects of a passive back-support exoskeleton. Our presented technology may lead to a new class of bio-protective robots in which personalized assistance profiles are provided based on subject-specific musculoskeletal variables.

Musculoskeletal-Modeling-Based, Full-Body Load-Assessment Tool for Ergonomists (MATE): Method Development and Proof of Concept Case Studies.

A new ergonomic-risk-assessment tool was developed that combines musculoskeletal-model-based loading estimates with insights from fatigue failure theory to evaluate full-body musculoskeletal loading during dynamic tasks. Musculoskeletal-modeling output parameters, i.e., joint contact forces and muscle forces, were combined with tissue-specific injury thresholds that account for loading frequency to determine the injury risk for muscles, lower back, and hip cartilage. The potential of this new risk-assessment tool is demonstrated for defining ergonomic interventions in terms of lifting characteristics, back and shoulder exoskeleton assistance, box transferring, stoop lifting, and an overhead wiring task, respectively. The MATE identifies the risk of WMSDs in different anatomical regions during occupational tasks and allows for the evaluation of the impact of interventions that modify specific lifting characteristics, i.e., load weight versus task repetition. Furthermore, and in clear contrast to currently available ergonomic assessment scores, the effects of the exoskeleton assistance level on the risk of WMSDs of full-body musculoskeletal loading (in particular, the muscles, lower back, and hips) can be evaluated and shows small reductions in musculoskeletal loading but not in injury risk. Therefore, the MATE is a risk-assessment tool based on a full-body, musculoskeletal-modeling approach combined with insights from the fatigue failure theory that shows the proof of concept of a shoulder and back exoskeleton. Furthermore, it accounts for subject-specific characteristics (age and BMI), further enhancing individualized ergonomic-risk assessment.

Using passive or active back-support exoskeletons during a repetitive lifting task: influence on cardiorespiratory parameters

The objective of this laboratory study was to assess the cardiorespiratory consequences related to the use of different back-support exoskeletons during a repetitive lifting task. Fourteen women and thirteen men performed a dynamic stoop lifting task involving full flexion/extension of the trunk in the sagittal plane. This task was repeated for 5min with a 10kg load to handle. Four conditions were tested: with a passive exoskeleton (P-EXO), with two active exoskeletons (A-EXO1 and A-EXO2), as well as without exoskeleton (FREE). The oxygen consumption rate and cardiac costs were measured continuously. Results showed a significantly lower (p < 0.05) oxygen consumption rate for all exoskeletons as compared to FREE (12.6 ± 2.2ml/kg/min). The values were also significantly lower (p < 0.001) for A-EXO1 (9.1 ± 1.8ml/kg/min) compared to A-EXO2 (11.0 ± 1.8ml/kg/min) and P-EXO (11.8 ± 2.4ml/kg/min). Compared to FREE (59.7 ± 12.9bpm), the cardiac cost was significantly reduced (p < 0.001) only for A-EXO1 (45.1 ± 11.5bpm). Several factors can explain these differences on the cardiorespiratory parameters observed between exoskeletons: the technology used (passive vs active), the torque provided by the assistive device, the weight of the system, but also the level of anthropomorphism (related to the number of joints used by the exoskeleton). Our results also highlighted the lack of interaction between the exoskeleton and sex. Thereby, the three back-support exoskeletons tested appeared to reduce the overall physical workload associated with a repetitive lifting task both for men and women.

The Stoop-Squat-Index: a simple but powerful measure for quantifying whole-body lifting behavior

BackgroundMost of the studies evaluating lifting behavior only focus on very localized parameters such as lumbar spine flexion, while evaluations of whole-body strategies are largely lacking. To enable relatively simple evaluations of whole-body strategies, this study aimed at developing a novel index for quantifying the stoop-squat behavior, and to establish normative values of the index for healthy pain-free adults.MethodsA novel index, the Stoop-Squat-Index, was developed, which describes the proportion between trunk forward lean and lower extremity joint flexion, with possible values ranging from 0 (full squat lifting) to 100 (full stoop lifting). To enable the interpretation of the index in a real-life setting, normative values for lifting a moderately-weighted object (15-kg-box) with a full squat and a full stoop technique were established using motion capture data from 30 healthy pain-free individuals that underwent motion analysis of squat and stoop lifting in the context of a previously conducted study.ResultsThe results showed mean index values of lower than 30 and higher than 90 for the most relevant phases of the squat and stoop movements, respectively, with mean index values differing significantly from each other for the full duration of the lifting phases.ConclusionsThe main advantages of the index are that it is simple to calculate and can not only be derived from motion capture data but also from conventional video recordings, which enables large-scale in-field measurements with relatively low expenditure. When used in combination with lumbar spine flexion measurements, the index can contribute important information, which is necessary for comprehensively evaluating whole-body lifting strategies and to shed more light on the debate over the connection between lifting posture and back complaints.

SEMG-Triggered Fast Assistance Strategy for a Pneumatic Back Support Exoskeleton.

To prevent lower back pain (LBP) in the industrial workplace, various powered back support exoskeletons (BSEs) have been developed. However, conventional kinematics-triggered assistance (KA) strategies induce latency, degrading assistance efficiency. Therefore, we proposed and experimentally evaluated a surface electromyography (sEMG)-triggered assistance (EA) strategy. Nine healthy subjects participated in the lifting experiments: 1) external loads test, 2) extra latency test, and 3) repetitive lifting test. In the external loads test, subject performed lifting with four different external loads (0 kg, 7.5 kg, 15 kg, and 22.5 kg). The assistance was triggered earlier by EA compared to KA from 114 ms to 202 ms, 163 ms to 269 ms for squat and stoop lifting respectively, as external loads increased from 0 kg to 22.5 kg. In the extra latency test, the effects of extra latency (manual switch, 0 ms, 100 ms and 200 ms) in EA on muscle activities were investigated. Muscle activities were minimized in the fast assistance (0 ms and 100 ms) condition and increased with extra latency. In the repetitive lifting test, the EA strategy significantly reduced L1 muscle fatigue by 70.4% in stoop lifting, compared to KA strategy. Based on the experimental results, we concluded that fast assistance triggered by sEMG improved assistance efficiency in BSE and was particularly beneficial in heavy external loads situations. The proposed assistive strategy can be used to prevent LBP by reducing back muscle fatigue and is easily applicable to various industrial exoskeleton applications.

Lifting Techniques: Why Are We Not Using Evidence To Optimize Movement?

Lifting something off the ground is an essential task and lifting is a documented risk factor for low back pain (LBP). The standard lifting techniques are stoop (lifting with your back), squat (lifting with your legs), and semi-squat (midway between stoop and squat). Most clinicians believe the squat technique is optimal; however, training on squat lifting does not prevent LBP and utilizing greater lumbar flexion (i.e. stoop) when lifting is not a risk factor for LBP. The disconnect between what occurs in clinical practice and what the evidence suggests has resulted in ongoing debate. Clinicians must ask the right questions in order to apply the evidence appropriately. A proposed clinical framework of calm tissue down, build tissue up, improve work capacity can be used to determine which lifting technique is optimal for a patient at any given time. When applying this clinical framework, clinicians should consider metabolic, biomechanical, physical stress tolerance, and pain factors in order to address the movement system. For example, stoop lifting is more metabolically efficient and less challenging to the cardiopulmonary system. There may be few biomechanical differences in spinal postures and gross loads on the lumbar spine between stoop, squat, and semi-squat lifting; however, each lift has distinct kinematic patterns that affects muscle activation patterns, and ultimately the movement system. Clinicians must find the optimal dosage of physical stress to address all aspects of the movement system to minimize the risk of injury. There is no universal consensus on the optimal lifting technique which will satisfy every situation; however, there may be a lifting technique that optimizes movement to achieve a specific outcome. The calm tissue down, build tissue up, improve work capacity framework offers an approach to determine the best lifting technique for an individual patient at any give time.Level of Evidence5

From Stoop to Squat: A Comprehensive Analysis of Lumbar Loading Among Different Lifting Styles.

Lifting up objects from the floor has been identified as a risk factor for low back pain, whereby a flexed spine during lifting is often associated with producing higher loads in the lumbar spine. Even though recent biomechanical studies challenge these assumptions, conclusive evidence is still lacking. This study therefore aimed at comparing lumbar loads among different lifting styles using a comprehensive state-of-the-art motion capture-driven musculoskeletal modeling approach. Thirty healthy pain-free individuals were enrolled in this study and asked to repetitively lift a 15 kg-box by applying 1) a freestyle, 2) a squat and 3) a stoop lifting technique. Whole-body kinematics were recorded using a 16-camera optical motion capture system and used to drive a full-body musculoskeletal model including a detailed thoracolumbar spine. Continuous as well as peak compressive, anterior-posterior shear and total loads (resultant load vector of the compressive and shear load vectors) were calculated based on a static optimization approach and expressed as factor body weight (BW). In addition, lumbar lordosis angles and total lifting time were calculated. All parameters were compared among the lifting styles using a repeated measures design. For each lifting style, loads increased towards the caudal end of the lumbar spine. For all lumbar segments, stoop lifting showed significantly lower compressive and total loads (−0.3 to −1.0BW) when compared to freestyle and squat lifting. Stoop lifting produced higher shear loads (+0.1 to +0.8BW) in the segments T12/L1 to L4/L5, but lower loads in L5/S1 (−0.2 to −0.4BW). Peak compressive and total loads during squat lifting occurred approximately 30% earlier in the lifting cycle compared to stoop lifting. Stoop lifting showed larger lumbar lordosis range of motion (35.9 ± 10.1°) than freestyle (24.2 ± 7.3°) and squat (25.1 ± 8.2°) lifting. Lifting time differed significantly with freestyle being executed the fastest (4.6 ± 0.7 s), followed by squat (4.9 ± 0.7 s) and stoop (5.9 ± 1.1 s). Stoop lifting produced lower total and compressive lumbar loads than squat lifting. Shear loads were generally higher during stoop lifting, except for the L5/S1 segment, where anterior shear loads were higher during squat lifting. Lifting time was identified as another important factor, considering that slower speeds seem to result in lower loads.

Multijoint passive elastic spine exoskeleton for stoop lifting assistance

In the task of carrying heavy objects, it is easy to cause back injuries and other musculoskeletal diseases. Although wearable robots are designed to reduce this danger, most existing exoskeletons use high-stiffness mechanisms, which are beneficial to load-bearing conduction, but this restricts the natural movement of the human body, thereby causing ergonomic risks. This article proposes a back exoskeleton composed of multiple elastic spherical hinges inspired by the biological spine. This spine exoskeleton can assist in the process of bending the body and ensure flexibility. We deduced the kinematics model of this mechanism and established an analytical biomechanical model of human–robot interaction. The mechanism of joint assistance of the spine exoskeleton was discussed, and experiments were conducted to verify the flexibility of the spine exoskeleton and the effectiveness of the assistance during bending.

Kinematic effects of a passive lift assistive exoskeleton

The VT-Lowe’s exoskeleton was designed to help support the back during repetitive lifting tasks. This study focused on the kinematic differences between lifting with and without the exoskeleton (With-Exo and Without-Exo) over three different lifting styles (Freestyle, Squat, and Stoop) and two different box weights (0% and 20% of bodyweight). Twelve young and healthy males (Age 23.5 +/− 4.42 years; Height 179.33 +/− 6.37 cm; Weight 80.4 +/− 5.59 kg) participated in this study. Variables analyzed include the ankle and knee angles and angle between the Shoulder-Hip-Knee (SHK); the shoulder, elbow, and wrist heights; and the lifting speed and acceleration. The relationships between the torso angle, SHK angle, center of mass of the torso, torso torque, box height, as well as electromyography (EMG) data from a related study were also analyzed. On average, wearing the exoskeleton resulted in a 1.5 degree increase in ankle dorsiflexion, a 2.6 degree decrease in knee flexion, and a decrease of 2.3 degrees in SHK angle. Subjects’ shoulder, elbow, and wrist heights were slightly higher while wearing the exoskeleton, and they lifted slightly more slowly while wearing the exoskeleton. Subjects moved more quickly while bending down as compared to standing up, and with the 0% bodyweight box as compared to the 20% bodyweight box. The values for Freestyle lifts generally fell in between Squat and Stoop lift styles or were not significantly different from Squat. EMG data from the leg muscles had relationships with torso torque while the back and stomach muscles showed no significant relationships.

Electromyography (EMG) for Assessment in Low Back Pain; Erector Spinae Muscle

The purpose of electromyography (EMG) study is to identify which position of surface EMG sensor attached to erector spinae muscle related with lower back pain by squat and stoop lifting technique. This is to avoid lower back pain (LBP) occur during Manual Materials Handling (MMH). There are only one types of upper extremity muscle were chosen to be monitored in this study which is erector spinae (ES) muscle with different electrodes placement on the surface electromyography (sEMG) sensor. However, each of the lifting styles come out with the different reading of root mean square (RMS) frequency for each muscle chosen. In this study, the two subjects consist of two females with normal body mass index (BMI) range from 18.5 to 24.9 with same physical measurement, was selected in order to perform both styles of lifting which are squatting and stooping. For every session the subject will undertake 15 repetitions with 15 minutes rest in between for each movement. In furtherance of to get the analysis muscle activity, proEMG software is used. The results of study for subject female 1 showed that the squat technique had higher levels of muscle activation compared to stoop technique on left erector spinae (LES) muscle. However, the LES muscle activation for subject 2 is greater at stoop activity. On the contrary, squat technique had lower fatigue analysis compared to stoop technique for both subjects on LES muscle. Conclusion, squat technique is better than stoop technique but stoop lift is more natural and spontaneously used for MMH.

A braced arm-to-thigh (BATT) lifting technique reduces lumbar spine loads in healthy and low back pain participants

Despite the common use of one-handed lifting techniques for activities of daily living, these techniques have received little attention in the biomechanics literature. The braced arm-to-thigh technique (BATT) is a one-handed lifting method in which the dominant hand picks up objects, while the free hand braces the trunk on the ipsilateral thigh. The aim of this study was to compare the BATT to two-handed or unsupported one-handed lifting techniques with loads of 2 and 10 kg, by evaluating trunk motion and spine loading at L4/L5. Twenty healthy participants (30–70 years old) matched in age and sex to 18 participants with low back pain were recruited to the study. A three-axis load cell secured to the distal anterior thigh measured the bracing forces applied by the hand. The OpenSim Lifting Full-Body model was used to estimate trunk kinematics and spinal loading at L4/L5. Linear mixed-effects models were developed to compare trunk angles and L4/L5 moments and forces between lifting techniques. Trunk flexion angles were significantly reduced for the BATT lift compared to one-handed and two-handed stoop lifts (9–20%). However, the BATT also increased asymmetric trunk kinematics and moments at L4/L5. The BATT produced significantly lower moments (28–38%), and compressive (25–32%) and antero-posterior shear (25–45%) forces at L4/L5, compared to unsupported lifting techniques. Bracing the hand on the thigh to support the trunk can substantially reduce low back loading during lifting tasks of 2 to 10 kg.

Finite Element Analysis of L3-L4 Vertebrae at Various Body Postures Under Osteoporotic Condition

The present study gives detailed quantitative information on the biomechanical behavior of osteoporotic vertebra which can be used in the evaluation of osteoporotic bone fracture. A three -dimensional finite element model has been developed and applied for the analysis of the lumbar L3-L4 disc -body unit under various body postures like normal standing, extension, weight lifting, stoop lift and squat lift. The stress and deformation of disc and vertebrae are compared with the corresponding values at 10% and 20% osteoporotic condition. It is observed that for weight lifting, stoop lifting, and squat lift under 20% osteoporotic condition the maximum value of stress obtained is close to compressive strength of the bone. Also noted that fewer than 20% osteoporotic condition the deformation of the disc is increased to 20.08% under weight lifting postures. Therefore,we come to the conclusion that people at old age should avoid risky postures which result in low back pain or lumbar fracture.

Design and Control of a High-Torque and Highly Backdrivable Hybrid Soft Exoskeleton for Knee Injury Prevention During Squatting

This letter presents design and control innovations of wearable robots that tackle two barriers to widespread adoption of powered exoskeletons: restriction of human movement and versatile control of wearable co-robot systems. First, the proposed high torque density actuation comprised of our customized high-torque density motors and low ratio transmission mechanism significantly reduces the mass of the robot and produces high backdrivability. Second, we derive a biomechanics model-based control that generates assistive torque profile for versatile control of both squat and stoop lifting assistance. The control algorithm detects lifting postures using compact inertial measurement unit (IMU) sensors to generate an assistive profile that is proportional to the human joint torque produced from our model. Experimental results demonstrate that the robot exhibits low mechanical impedance (1.5 Nm backdrive torque) when it is unpowered and 0.5 Nm backdrive torque with zero-torque tracking control. Root mean square (RMS) error of torque tracking is less than 0.29 Nm (1.21% error of 24 Nm peak torque). Compared with squatting without the exoskeleton, the controller reduces 87.5%, 80% and 75% of the three knee extensor muscles (average peak EMG of 3 healthy subjects) during squat with 50% of human joint torque assistance.