Haemodynamics and oxygenation in the lower-limb muscles of young ambulatory adults with cerebral palsy.

AIM: To evaluate muscle haemodynamics and oxygen metabolism in adults with cerebral palsy (CP) at rest and during exercise. METHOD: This cross-sectional study included 12 adults with spastic CP (four females, eight males; mean age [SD] 29 years 6 months [7 years 10.8 months]) and 13 typically developing individuals (seven females, six males; mean age [SD] 26 years 6 months [1 year 1.9 months]). Near-infrared spectroscopy was used to assess changes in muscle blood flow (mBF), muscle oxygen consumption (mVO2 ), and muscle oxygen saturation in the vastus lateralis and rectus femoris muscles during three conditions: rest, low load at 20% maximum voluntary contraction (MVC), and high load at 80% MVC. RESULTS: MBF was lower in participants with CP than in typically developing participants at rest (p < 0.001) and at 20% MVC (p = 0.007) in both muscles. Increased load caused a reduction in mBF in typically developing participants and an increase in CP. MVO2 in typically developing participants increased from rest to 20% MVC and was reduced at 80% MVC compared with 20% MVC. In participants with CP, there was no change with load in the rectus femoris muscle; however, there was an increase in the vastus lateralis muscle from rest to 20% MVC, and 80% MVC had a similar value. Muscle saturation was higher in participants with CP across all conditions (vastus lateralis, p < 0.001; rectus femoris, p = 0.0518). INTERPRETATION: Oxidative metabolism in CP is not limited by oxygen delivery (mBF), because high muscle saturation suggests oxygen availability. Adults with CP demonstrate muscular responses to exercise that are inconsistent with typical high-workload activation, probably because of inefficient fibre recruitment and secondary anomalies.

Temporal But Not Spatial Gait Parameters Associated With Lower Balance Capacity in Moderate-High Functioning Persons With Stroke.

BACKGROUND AND PURPOSE: Falls are a major health concern after stroke. Spatial and temporal gait asymmetry and variability can contribute to instability and increased fall risk in persons with stroke (PwS). We aimed to quantify gait spatiotemporal symmetry and variability parameters in PwS undergoing rehabilitation in the subacute stage of the disease, by comparison to healthy participants, and to examine the associations between these parameters and patients' reactive and proactive balance capacity. METHODS: Twenty-two PwS and 12 healthy adults walked over a computerized treadmill system at their self-selected walking speed. Symmetry and variability of gait parameters (step length, swing time, and stance time) as well as upper extremity and lower extremity angular range of motion in the sagittal plane were extracted. In addition, the Berg Balance Scale (BBS) and the fall threshold in response to sudden surface translations at increasing intensities were assessed. RESULTS: PwS demonstrated significantly higher asymmetry in all gait parameters in comparison to controls. Also, PwS demonstrated increased stance time variability in comparison to healthy controls and increased swing time variability in the paretic lower extremity. Significant negative associations were found between fall threshold and stance time asymmetry in PwS (r = -0.48, P = 0.022), between the BBS and swing time asymmetry (r = -0.50, P = 0.018), and between the BBS and stance time variability of the paretic lower extremity (r = -0.56, P = 0.006). DISCUSSION AND CONCLUSIONS: Findings highlight the importance of gait temporal symmetry and variability measures for dynamic balance control after stroke. These parameters should be considered when assessing gait recovery and safety in PwS.Video Abstract available for more insight from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A355).

Shoulder moments and angles during single and combined manual material handling tasks.

Shoulder musculoskeletal disorders due to manual material handling tasks are common workplace injuries. Here we investigated the difference in shoulder biomechanics (moments and angles) between a single task of removing a box from a shelf (or depositing a box on a shelf) and the equivalent part of a combined task that consisted of removing, carrying and depositing boxes; that is, a single removing [depositing] task was compared with the removing [depositing] part of a combined task. We found that the peak and cumulative shoulder moments were larger during the single-task paradigm than during the equivalent part of the combined task by 26.3 and 25.5%, respectively. The two paradigms also differed in terms of shoulder angles. It is likely that the main contributors to this overestimation were differences between the single and combined tasks in terms of the lever arm (i.e. horizontal distance), the shoulder angle, and the task duration. Practitioners' Summary: We investigated shoulder moments during single and combined manual material handling tasks. Shoulder moments were found to be smaller during combined tasks. Practitioners should consider that analysing combined tasks using estimations based on single tasks could result in an overestimation of 26.3 and 25.5% in peak and cumulative shoulder moments, respectively.Abbrevaitions: MSDs: musculoskeletal disorders; MMH: manual material handling; LMM: linear mixed model.

Preferred walking speed on rough terrain: is it all about energetics?

Humans have evolved the ability to walk very efficiently. Further, humans prefer to walk at speeds that approximately minimize their metabolic energy expenditure per unit distance (i.e. gross cost of transport, COT). This has been found in a variety of population groups and other species. However, these studies were mostly performed on smooth, level ground or on treadmills. We hypothesized that the objective function for walking is more complex than only minimizing the COT. To test this idea, we compared the preferred speeds and the relationships between COT and speed for people walking on both a smooth, level floor and a rough, natural terrain trail. Rough terrain presumably introduces other factors, such as stability, to the objective function. Ten healthy men walked on both a straight, flat, smooth floor and an outdoor trail strewn with rocks and boulders. In both locations, subjects performed five to seven trials at different speeds relative to their preferred speed. The COT-speed relationships were similarly U-shaped for both surfaces, but the COT values on rough terrain were approximately 115% greater. On the smooth surface, the preferred speed (1.24±0.17 m s-1) was not found to be statistically different (P=0.09) than the speed that minimized COT (1.34±0.03 m s-1). On rough terrain, the preferred speed (1.07±0.05 m s-1) was significantly slower than the COT minimum speed (1.13±0.07 m s-1; P=0.02). Because near the optimum speed the COT function is very shallow, these changes in speed result in a small change in COT (0.5%). It appears that the objective function for speed preference when walking on rough terrain includes COT and additional factors such as stability.

Gait Coordination Deteriorates in Independent Old-Old Adults.

Human gait is symmetric and bilaterally coordinated in young healthy persons. In this study, we aimed to explore the differences in bilateral coordination of gait as measured by the phase coordination index (PCI), gait asymmetry, and stride time variability of gait between four age groups. A total of 44 older adults were recruited: nine young-old (age 70-74 years), 26 old (age 75-84 years), nine old-old (>85 years and older), and 13 young adults (age 20-30 years). Subjects walked on a treadmill; walking speed was systematically increased from 0.5 to 0.9 m/s in steps of 0.1 m/s. There were marginal effects of age on PCI, significant main effects of walking speeds without interaction between walking speeds and age group. A difference in PCI could distinguish between young's and late aging group, and only during their preferred treadmills walking speed. This study explicitly shows that bilateral coordination of walking is modified by gait speed, and deteriorates only at a very old age.

Harvesting biomechanical energy or carrying batteries? An evaluation method based on a comparison of metabolic power.

BACKGROUND: Harvesting energy from human motion is an innovative alternative to using batteries as a source of electrical power for portable devices. Yet there are no guidelines as to whether energy harvesting should be preferred over batteries. This paper introduces an approach to determine which source of energy should be preferred. The proposed approach compares the metabolic power while harvesting energy and while using batteries (or any other power supply, e.g., solar panels), which provide equal amount of energy. Energy harvesting is preferred over batteries if the metabolic power required to harvest the energy is lower than that required to carry the batteries. Metabolic power can be experimentally measured. However, for design purposes, it is essential to assess differences in metabolic power as a function of the device parameters. THE MODEL: To this end, based on the proposed approach, we develop a mathematical model that considers the following parameters: the device's mass, its location on the human body, the electrical power output, cost of harvesting (COH), walking time, and the specific energy of the battery. METHOD: We apply the model in two ways. First, we conduct case studies to examine current ankle, knee, and back energy harvesting devices, and assess the walking times that would make these devices preferable over batteries. Second, we conduct a design scenarios analysis, which examines future device developments. RESULTS: The case studies reveal that to be preferred over batteries, current harvesting devices located on the ankle, knee, or back would require walking for 227 hours, 98 hours, or 260 hours, respectively. This would replace batteries weighing 6.81 kg (ankle), 5.88 kg (knee), or 2.6 kg (back). The design scenarios analysis suggests that for harvesting devices to be beneficial with less than 25 walking hours, future development should focus on light harvesting devices (less than 0.2 kg) with low COH (equal or lower than 0). Finally, a comparison with portable commercial solar panels reveals that under ideal sun exposure conditions, solar panels outperform the current harvesting devices. CONCLUSIONS: Our model offers a tool for assessing the performance of energy harvesting devices.

Rethinking Exoskeleton Simulation-Based Design: The Effect of Using Different Cost Functions.

Designing an exoskeleton that can improve user capabilities is a challenging task, and most designs rely on experiments to achieve this goal. A different approach is to use simulation-based designs to determine optimal device parameters. Most of these simulations use full trajectory tracking limb kinematics during a natural gait as a reference. However, exoskeletons typically change the natural gait kinematics of the user. Other types of simulations assume that human gait is optimized for a cost function that combines several objectives, such as the cost of transport, injury prevention, and stabilization. In this study, we use a 2D OpenSim model consisting of 10 degrees of freedom and considering 18 muscles, together with the Moco optimization tool, to investigate the differences between these two approaches with respect to running with a passive knee exoskeleton. Utilizing this model, we test the effect of a full trajectory tracking objective with different weights (representing the importance of the objective in the optimization cost function) and show that when using weights that are typically used in the literature, there is no deviation from the experimental data. Next, we develop a multi-objective cost function with foot clearance term based on peak knee angle during swing, that achieves trajectories similar (RMSE=7.4 deg) to experimental running data. Finally, we investigate the effect of different parameters in the design of a clutch-based passive knee exoskeleton (1.5 kg at each leg) and find that a design that utilizes a 2.5 Nm/deg spring achieves an improvement of up to 8% in net metabolic energy. Our results show that tracking objectives in the cost function, even with a low weight, hinders the simulation's ability to change the gait trajectory. Thus, there is a need for other predictive simulation methods for exoskeletons.

Emotion and motion: Toward emotion recognition based on standing and walking.

Emotion recognition is key to interpersonal communication and to human-machine interaction. Body expression may contribute to emotion recognition, but most past studies focused on a few motions, limiting accurate recognition. Moreover, emotions in most previous research were acted out, resulting in non-natural motion, which is unapplicable in reality. We present an approach for emotion recognition based on body motion in naturalistic settings, examining authentic emotions, natural movement, and a broad collection of motion parameters. A lab experiment using 24 participants manipulated participants' emotions using pretested movies into five conditions: happiness, relaxation, fear, sadness, and emotionally-neutral. Emotion was manipulated within subjects, with fillers in between and a counterbalanced order. A motion capture system measured posture and motion during standing and walking; a force plate measured center of pressure location. Traditional statistics revealed nonsignificant effects of emotions on most motion parameters; only 7 of 229 parameters demonstrate significant effects. Most significant effects are in parameters representing postural control during standing, which is consistent with past studies. Yet, the few significant effects suggest that it is impossible to recognize emotions based on a single motion parameter. We therefore developed machine learning models to classify emotions using a collection of parameters, and examined six models: k-nearest neighbors, decision tree, logistic regression, and the support vector machine with radial base function and linear and polynomial functions. The decision tree using 25 parameters provided the highest average accuracy (45.8%), more than twice the random guess for five conditions, which advances past studies demonstrating comparable accuracies, due to our naturalistic setting. This research suggests that machine learning models are valuable for emotion recognition in reality and lays the foundation for further progress in emotion recognition models, informing the development of recognition devices (e.g., depth camera), to be used in home-setting human-machine interactions.

Design of a Multi-Joint Passive Exoskeleton for Vertical Jumping Using Optimal Control.

Research and the commercial use of exoskeletons that augment human activities are rapidly growing. However, the progress of the two is hindered by the time-consuming and costly process of designing and evaluating the exoskeleton. One of the solutions to reduce both is the use of simulations that model the users, exoskeleton, and their interaction. At the same time, most simulations focus on continuous tasks, such as walking, running, and industrial activities. The augmentation of human capability is essential in fast motion tasks (i.e., jumping, throwing), where the muscles are producing their maximum force. Thus, this study implemented a simulation of passive exoskeleton-human interactions using OpenSim and Moco software for optimal control to find muscle excitation that maximizes vertical jump height. The models include a planar human model with ankle, knee, and hip joints. The muscles were modeled as torque actuators for each joint, with a flexor and an extensor, and passive torques representing each joint's ligaments. The simulation was used to study: a) the effect of different spring stiffness at the knee, hip, and ankle joints and combinations of these joints; b) multi-joints vs. single joints; c) the effect of an elliptic pulley and different initial engagement angle for springs. The results revealed that the jump height increased as the spring became stiffer, up to a maximum point. For a single joint, the knee exoskeleton was the most effective, compared with the hip and ankle joint exoskeletons. The multi-joint exoskeleton was slightly better than the single knee joint. If maximum spring tension is a limiting factor, an elliptic pulley has an advantage relative to a round pulley. An initial angle of engagement (with equal work) other than zero up to approximately 50 degrees does not decrease the jump height.

Passive Knee Exoskeleton Increases Vertical Jump Height.

Most exoskeletons are designed to reduce the metabolic costs of performing aerobic tasks such as walking, running, and hopping. This study presents an exoskeleton that boosts vertical jumping-a fast, short movement during which the muscles are exerted at peak capacity. It was hypothesized that a passive exoskeleton would increase vertical jump height without requiring external energy input. The device comprises springs that work in parallel with the muscles of the quadriceps femoris. The springs store mechanical energy during knee flexion (the negative work phase) and release that energy during the subsequent knee extension (the positive work phase), augmenting the muscles. Ten healthy participants were evaluated in two experimental sessions. In the first session, the participants jumped without receiving instructions on how to use the exoskeleton, and the results showed no difference in jump height when jumping with the exoskeleton or jumping without it. In the second session, the participants were instructed to achieve deeper initial squat heights at the start of the jump. This resulted in a 6.4% increase in average jump height compared to jumping without the exoskeleton (each participant performed five jumps for each the two conditions). This is the first time that a passive exoskeleton has been shown to improve the height of a vertical jump from a dead stop.

Effect of using real motion versus predicted motion as input for digital human modeling of back and shoulder loads during manual material handling.

Digital human modeling (DHM) technology is considered the state of the art in designing and evaluating workstations. Previous studies examined the differences between DHM's posture and motion prediction relative to human experimental data. Yet, the effect the two different inputs on biomechanical loads was not assessed. Therefore, this study evaluates the differences in L4/L5 compression force and shoulder torques during a work process calculated using DHM with motion prediction (Jack by Siemens) and DHM with experimental data. The work process is a sequential removing, carrying, and depositing task performed by nine females and nine males and recorded using a motion capture system. The analysis shows that using experimental data results in larger back compression force during the removing task (average 15.4%), similar force during the depositing task (average 0.68%), and less force during the carrying task (19.875%). Using experimental data resulted in larger shoulder torque during all tasks (average 24.97%).

Enhancing motion tracking accuracy of a low-cost 3D video sensor using a biomechanical model, sensor fusion, and deep learning.

Low-cost 3D video sensors equipped with routines for extracting skeleton data facilitate the widespread use of virtual reality (VR) for rehabilitation. However, the accuracy of the extracted skeleton data is often limited. Accuracy can be improved using a motion tracker, e.g., using a recurrent neural network (RNN). Yet, training an RNN requires a considerable amount of relevant and accurate training data. Training databases can be obtained using gold-standard motion tracking sensors. This limits the use of the RNN trackers in environments and tasks that lack accessibility to gold-standard sensors. Digital goniometers are typically cheaper, more portable, and simpler to use than gold-standard motion tracking sensors. The current work suggests a method for generating accurate skeleton data suitable for training an RNN motion tracker based on the offline fusion of a Kinect 3D video sensor and an electronic goniometer. The fusion applies nonlinear constraint optimization, where the constraints are based on an advanced shoulder-centered kinematic model of the arm. The model builds on the representation of the arm as a triangle (the arm triangle). The shoulder-centered representation of the arm triangle motion simplifies constraint representation and consequently the optimization problem. To test the performance of the offline fusion and the RNN trained using the optimized data, arm motion of eight participants was recorded using a Kinect sensor, an electronic goniometer, and, for comparison, a passive-marker-based motion tracker. The data generated by fusing the Kinect and goniometer recordings were used for training two long short-term memory (LSTM) RNNs. The input to one RNN included both the Kinect and the goniometer data, and the input to the second RNN included only Kinect data. The performance of the networks was compared to the performance of a tracker based on a Kalman filter and to the raw Kinect measurements. The accuracy of the fused data was high, and it considerably improved data accuracy. The accuracy for both trackers was high, and both were more accurate than the Kalman filter tracker and the raw Kinect measurements. The developed methods are suitable for integration with immersive VR rehabilitation systems in the clinic and the home environments.

Parametric equations to study and predict lower-limb joint kinematics and kinetics during human walking and slow running on slopes.

Comprehensive data sets for lower-limb kinematics and kinetics during slope walking and running are important for understanding human locomotion neuromechanics and energetics and may aid the design of wearable robots (e.g., exoskeletons and prostheses). Yet, this information is difficult to obtain and requires expensive experiments with human participants in a gait laboratory. This study thus presents an empirical mathematical model that predicts lower-limb joint kinematics and kinetics during human walking and running as a function of surface gradient and stride cycle percentage. In total, 9 males and 7 females (age: 24.56 ± 3.16 years) walked at a speed of 1.25 m/s at five surface gradients (-15%, -10%, 0%, +10%, +15%) and ran at a speed of 2.25 m/s at five different surface gradients (-10%, -5%, 0%, +5%, +10%). Joint kinematics and kinetics were calculated at each surface gradient. We then used a Fourier series to generate prediction equations for each speed's slope (3 joints x 5 surface gradients x [angle, moment, mechanical power]), where the input was the percentage in the stride cycle. Next, we modeled the change in value of each Fourier series' coefficients as a function of the surface gradient using polynomial regression. This enabled us to model lower-limb joint angle, moment, and power as functions of the slope and as stride cycle percentages. The average adjusted R2 for kinematic and kinetic equations was 0.92 ± 0.18. Lastly, we demonstrated how these equations could be used to generate secondary gait parameters (e.g., joint work) as a function of surface gradients. These equations could be used, for instance, in the design of exoskeletons for walking and running on slopes to produce trajectories for exoskeleton controllers or for educational purposes in gait studies.

Biomechanical knee energy harvester: Design optimization and testing.

Biomechanical energy harvesters are designed to generate electrical energy from human locomotion (e.g., walking) with minimal or no additional effort by the users. These harvesters aim to carry out the work of the muscles during phases in locomotion where the muscles are acting as brakes. Currently, many harvesters focus on the knee joint during late swing, which is only one of three phases available during the gait cycle. For the device to be successful, there is a need to consider design components such as the motor/generator and the gear ratio. These components influence the amount of electrical energy that could be harvested, metabolic power during harvesting, and more. These various components make it challenging to achieve the optimal design. This paper presents a design of a knee harvester with a direct drive that enables harvesting both in flexion and extension using optimization. Subsequently, two knee devices were built and tested using five different harvesting levels. Results show that the 30% level was the best, harvesting approximately 5 W of electricity and redacting 8 W of metabolic energy compared to walking with the device as a dead weight. Evaluation of the models used in the optimization showed a good match to the system model but less for the metabolic power model. These results could pave the way for an energy harvester that could utilize more of the negative joint power during the gait cycle while reducing metabolic effort.

Biomechanical energy harvesting from human motion: theory, state of the art, design guidelines, and future directions.

BACKGROUND: Biomechanical energy harvesting from human motion presents a promising clean alternative to electrical power supplied by batteries for portable electronic devices and for computerized and motorized prosthetics. We present the theory of energy harvesting from the human body and describe the amount of energy that can be harvested from body heat and from motions of various parts of the body during walking, such as heel strike; ankle, knee, hip, shoulder, and elbow joint motion; and center of mass vertical motion. METHODS: We evaluated major motions performed during walking and identified the amount of work the body expends and the portion of recoverable energy. During walking, there are phases of the motion at the joints where muscles act as brakes and energy is lost to the surroundings. During those phases of motion, the required braking force or torque can be replaced by an electrical generator, allowing energy to be harvested at the cost of only minimal additional effort. The amount of energy that can be harvested was estimated experimentally and from literature data. Recommendations for future directions are made on the basis of our results in combination with a review of state-of-the-art biomechanical energy harvesting devices and energy conversion methods. RESULTS: For a device that uses center of mass motion, the maximum amount of energy that can be harvested is approximately 1 W per kilogram of device weight. For a person weighing 80 kg and walking at approximately 4 km/h, the power generation from the heel strike is approximately 2 W. For a joint-mounted device based on generative braking, the joints generating the most power are the knees (34 W) and the ankles (20 W). CONCLUSIONS: Our theoretical calculations align well with current device performance data. Our results suggest that the most energy can be harvested from the lower limb joints, but to do so efficiently, an innovative and light-weight mechanical design is needed. We also compared the option of carrying batteries to the metabolic cost of harvesting the energy, and examined the advantages of methods for conversion of mechanical energy into electrical energy.

Investigation of 3D dynamic and quasistatic models for spinal moments during combined manual material handling tasks.

Digital human modeling software uses biomechanical models to compute workers' risk of injury during industrial work processes. In many cases, the biomechanics are calculated using quasistatic models, which neglect the body's dynamics and therefore might be erroneous. This study investigated the differential effect of using a dynamic vs. a quasistatic model on spinal loading during combined manual material handling tasks that are prevalent in industry. An experiment was conducted involving nine male and nine female participants performing a total of 3402 cycles of a box-conveying task (removing, carrying and depositing) for different box masses and shelf heights. Using motion capture data, the peak and cumulative moments acting on the L5/S1 joint were calculated using 3D dynamic and quasistatic models. This revealed that neglecting the dynamic movements (i.e., using a quasistatic model) results in an on average underestimation of 19.7% in the peak spinal moment and 3.6% in the cumulative moment that in some cases exceeds the maximal limit for the compression forces acting on the lower back.

Simulation of a Passive Knee Exoskeleton for Vertical Jump Using Optimal Control.

Research on exoskeletons designed to augment human activities and the attendant exoskeleton industry are both rapidly growing areas of endeavor. However, progress in the field is currently being hindered by a lack of understanding of human-exoskeleton interactions. At present, the main method applied to reach such an understanding is to build and test prototypes or end-effectors (that simulate the devices), but this is a very time-consuming and costly process. In this study, we aimed to address this problem by simulating passive exoskeleton-human interactions during a vertical jump. The simulation is based on theoretical and empirical models. Using the simulation, we performed a numerical optimization procedure to determine the muscle excitations and starting postures that would give the maximum jump height. The simulation used a planar 4-DOF dynamic model. The muscles at the joints were modeled as torque actuators, with a flexor and an extensor for each joint and passive torque representing the tendon and muscle properties. We then simulated jumps with a passive knee exoskeleton with five different values of stiffness with the aim to study their effect on the jump height. The optimal excitation for the maximum jump height was found by using a genetic algorithm (GA). To improve our optimization performance and to test the convergence of the GA, the GA optimization was performed several times. For each exoskeleton condition, the GA found the optimal jump more than 400 times, and out of these solutions the one that achieved the highest jump was chosen. The result revealed an increase in jump height as the spring became stiffer. In addition, it was found that the energy that was stored in the spring of the exoskeleton was not fully converted to jump height.

Workers' biomechanical loads and kinematics during multiple-task manual material handling.

This study investigated the biomechanical loads and kinematics of workers during multiple-task manual material handling (MMH) jobs, and developed prediction models for the moments acting on a worker's body and their peak joint angles. An experiment was conducted in which 20 subjects performed a total of 3780 repetitions of a box-conveying task. This task included continuous sequential removing, carrying and depositing of boxes weighing 2-12 kg. The subjects' motion was captured using motion-capture technology. The origin/destination height was the most influencing predictor of the spinal and shoulder moments and the peak trunk, shoulder and knee angles. The relationship between the origin/destination heights and the above parameters was nonlinear. The mass of the box, and the subject's height and mass, also influenced the spinal and shoulder moments. A tradeoff between the moments acting on the L5/S1 vertebrae and on the shoulder joint was found. Compared to the models developed in similar studies that focused on manual material handling (albeit under different conditions), the high-order prediction equation for peak spinal moment formulated in the present study was found to explain between 10% and 48% more variability in the moments. This suggests that using a high-order equation in future studies might improve the prediction.

Paramedic equipment bags: How their position during out-of-hospital cardiopulmonary resuscitation (CPR) affect paramedic ergonomics and performance.

This study investigates how the positions of paramedic equipment bags affect paramedic performance and biomechanical loads during out-of-hospital Cardiopulmonary Resuscitation (CPR). An experiment was conducted in which 12 paramedic teams (each including two paramedics) performed in-situ simulations of a cardiac-arrest scenario. CPR quality was evaluated using five standard resuscitation measures (i.e., pre- and post-shock pauses, and compression rate, depth and fraction). The spinal loads while lifting, pulling and pushing the equipment bags were assessed using digital human modeling software (Jack) and prediction equation from previous studies. The results highlight where paramedics are currently choosing to position their equipment. They also demonstrate that the positions of the equipment bags affect CPR quality as well as the paramedics' work efficiency, physiological effort and biomechanical loads. The spinal loads ranged from 1901 to 4030N; furthermore, every occasion on which an equipment bag was lifted resulted in spinal forces higher than 3400N, thus exceeding the maximum threshold stipulated by the National Institute for Occupational Safety and Health. 72% of paramedics' postures were categorized as high or very high risk for musculoskeletal disorders by the Rapid Entire Body Assessment. Guidelines related to bag positioning and equipment handling might improve CPR quality and patient outcomes, and reduce paramedics' risk of injury.

Mechanics of walking and running up and downhill: A joint-level perspective to guide design of lower-limb exoskeletons.

Lower-limb wearable robotic devices can improve clinical gait and reduce energetic demand in healthy populations. To help enable real-world use, we sought to examine how assistance should be applied in variable gait conditions and suggest an approach derived from knowledge of human locomotion mechanics to establish a 'roadmap' for wearable robot design. We characterized the changes in joint mechanics during walking and running across a range of incline/decline grades and then provide an analysis that informs the development of lower-limb exoskeletons capable of operating across a range of mechanical demands. We hypothesized that the distribution of limb-joint positive mechanical power would shift to the hip for incline walking and running and that the distribution of limb-joint negative mechanical power would shift to the knee for decline walking and running. Eight subjects (6M,2F) completed five walking (1.25 m s-1) trials at -8.53°, -5.71°, 0°, 5.71°, and 8.53° grade and five running (2.25 m s-1) trials at -5.71°, -2.86°, 0°, 2.86°, and 5.71° grade on a treadmill. We calculated time-varying joint moment and power output for the ankle, knee, and hip. For each gait, we examined how individual limb-joints contributed to total limb positive, negative and net power across grades. For both walking and running, changes in grade caused a redistribution of joint mechanical power generation and absorption. From level to incline walking, the ankle's contribution to limb positive power decreased from 44% on the level to 28% at 8.53° uphill grade (p < 0.0001) while the hip's contribution increased from 27% to 52% (p < 0.0001). In running, regardless of the surface gradient, the ankle was consistently the dominant source of lower-limb positive mechanical power (47-55%). In the context of our results, we outline three distinct use-modes that could be emphasized in future lower-limb exoskeleton designs 1) Energy injection: adding positive work into the gait cycle, 2) Energy extraction: removing negative work from the gait cycle, and 3) Energy transfer: extracting energy in one gait phase and then injecting it in another phase (i.e., regenerative braking).