Sensitivity analysis and comparison of two methods of using heart rate to represent energy expenditure during walking

Karimi, Mohammad Taghi

doi:10.3233/wor-141845

Cited by 2 publications

(4 citation statements)

References 24 publications

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“…In our study, the heart rate was compared, as shown in Figure 13. The average heart rate (AHR) without robot assistance (AHR M ) and with robot assistance in our scheme (AHR R ) was used to estimate the metabolic rate in manual lifting tasks and robot-assisted lifting tasks, [37][38][39] as given in Table 6. The metabolic rate was different between the manual lifting and robot-assisted lifting tasks (ANOVA, p ¼ 0.049).…”

Section: Resultsmentioning

confidence: 99%

“…Generally, metabolic cost has been estimated by hemoglobin levels in the blood and by heart rate in previous studies. 5,38,39 However, these methods were used in aerobic exercises with strict standards. Apparently, these methods are not suitable for the lifting of an object at one time unless the experiment is repeated for a long time to create a change in the wearer's oxygen consumption.…”

Section: Discussionmentioning

confidence: 99%

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A safe human–robot interactive control structure with human arm movement detection for an upper-limb wearable robot used during lifting tasks

Hao

Zhao

et al. 2020

International Journal of Advanced Robotic Systems

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Manual lifting tasks involve repetitive raising, holding and stacking movements with heavy objects. These arm movements are notable risk factors for muscle pain, fatigue, and musculoskeletal disorders in workers. An upper-limb wearable robot, as a 6-DOF dual-arm exoskeleton, which was designed to augment workers’ strength and minimize muscular activation in the arm during repetitive lifting tasks. To adjust the robot joint trajectory, the user needs to apply an interactive torque to operate the robot during lifting tasks when a standard virtual mechanical impedance control structure is used. To reduce overshooting of the interactive torque on the user’s joint, a three-tier hierarchical control structure was developed for the robot in this study. At the highest level, a human arm movement detection module is used to detect the user’s arm motion according to the surface electromyography signals. Then, a Hammerstein adaptive virtual mechanical impedance controller is used at the middle level to reduce overshooting and yield an acceptable value of torque for the user’s elbow joint in actual lifting tasks. At the lowest level, the actuator controller on each joint of the robot controls the robot to complete lifting tasks. Several experiments were conducted, and the results showed that the interactive torque on the user’s elbow was limited and the muscular activations of erector spinae and biceps brachii muscles were effectively decreased. The proposed scheme prevents potential harm to the user due to excessive interactive torque on the human elbow joint, such as related muscle fatigue and joint injuries.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A safe human–robot interactive control structure with human arm movement detection for an upper-limb wearable robot used during lifting tasks

Hao

Zhao

et al. 2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…Accordingly, we hope to verify whether the new recovery gait is more suitable for the patient by detecting the energy expenditure. Clinically, there are multiple ways to detect the energy expenditure: measuring the rate of oxygen consumption is a good way assess the energy expenditure during walking; however, the instruments are cumbersome to wear and also restrict the normal walking (Karimi, 2015). Measuring the heart rate is another effective method to represent the energy expenditure approximately (Hiilloskorpi et al, 2003).…”

Section: Evaluation Criteriamentioning

confidence: 99%

“…Measuring the rate of oxygen consumption is a good way to assess the energy expenditure during walking. However, the instruments are cumbersome to wear and also restrict the normal walking (Karimi, 2015). Heart rate is an accurate and easy method to represent the energy expenditure during walking (Hiilloskorpi et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Deep Spatial-Temporal Model for rehabilitation gait: optimal trajectory generation for knee joint of lower-limb exoskeleton

Liu

Wang

et al. 2017

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Purpose The purpose of this paper is to model and predict suitable gait trajectories of lower-limb exoskeleton for wearer during rehabilitation walking. Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict suitable gait trajectories for wearer. Design/methodology/approach In this paper, the authors propose a Deep Spatial-Temporal Model (DSTM) for generating knee joint trajectory of lower-limb exoskeleton, which first leverages Long-Short Term Memory framework to learn the inherent spatial-temporal correlations of gait features. Findings With DSTM, the pathological knee joint trajectories can be predicted based on subject’s other joints. The energy expenditure is adopted for verifying the effectiveness of new recovery gait pattern by monitoring dynamic heart rate. The experimental results demonstrate that the subjects have less energy expenditure in new recovery gait pattern than in others’ normal gait patterns, which also means the new recovery gait is more suitable for subject. Originality/value Long-Short Term Memory framework is first used for modeling rehabilitation gait, and the deep spatial–temporal relationships between joints of gait data can obtained successfully.

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Sensitivity analysis and comparison of two methods of using heart rate to represent energy expenditure during walking

Abstract: Although the PCI and THBI indexes are easy to use and reliable parameters to represent the energy expenditure during walking, their sensitivity is not high to detect the influence of some orthotic interventions, such as use of insoles or using shoes on energy expenditure during walking.

Cited by 2 publications

References 24 publications

A safe human–robot interactive control structure with human arm movement detection for an upper-limb wearable robot used during lifting tasks

A safe human–robot interactive control structure with human arm movement detection for an upper-limb wearable robot used during lifting tasks

Deep Spatial-Temporal Model for rehabilitation gait: optimal trajectory generation for knee joint of lower-limb exoskeleton

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