The effects of different carrying methods on locomotion stability, gait spatio-temporal parameters and spinal stresses

Alamoudi, Mohammed; Travascio, Francesco; Onar‐Thomas, Arzu; Eltoukhy, Moataz; Asfour, Shihab

doi:10.1016/j.ergon.2018.04.012

Cited by 28 publications

(20 citation statements)

References 39 publications

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“…Gait symmetry can be expressed as a symmetry ratio of the right and left spatiotemporal gait parameters (i.e., step time, swing time, stance time, or step length). Since carrying an external load during walking may alter the body's center of mass (CoM) (Alamoudi et al, 2018) and disturb dynamic balance (Zultowski andAruin, 2008, Palumbo et al, 2001), a person with poor dynamic stability may demonstrate more gait asymmetry (Holbein andRedfern, 1994, Zultowski andAruin, 2008). Therefore, assessment of gait symmetry during load carrying may assess balance control and predict the risk of falls during such activities.…”

Section: Literature Reviewmentioning

confidence: 99%

Effects of load carrying techniques on gait parameters, dynamic balance, and physiological parameters during a manual material handling task

Anwer

Antwi‐Afari

et al. 2021

ECAM

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PurposeSince construction workers often need to carry various types of loads in their daily routine, they are at risk of sustaining musculoskeletal injuries. Additionally, carrying a load during walking may disturb their walking balance and lead to fall injuries among construction workers. Different load carrying techniques may also cause different extents of physical exertion. Therefore, the purpose of this paper is to examine the effects of different load-carrying techniques on gait parameters, dynamic balance, and physiological parameters in asymptomatic individuals on both stable and unstable surfaces.Design/methodology/approachFifteen asymptomatic male participants (mean age: 31.5 ± 2.6 years) walked along an 8-m walkway on flat and foam surfaces with and without a load thrice using three different techniques (e.g. load carriage on the head, on the dominant shoulder, and in both hands). Temporal gait parameters (e.g. gait speed, cadence, and double support time), gait symmetry (e.g. step time, stance time, and swing time symmetry), and dynamic balance parameters [e.g. anteroposterior and mediolateral center of pressure (CoP) displacement, and CoP velocity] were evaluated. Additionally, the heart rate (HR) and electrodermal activity (EDA) was assessed to estimate physiological parameters.FindingsThe gait speed was significantly higher when the load was carried in both hands compared to other techniques (Hand load, 1.02 ms vs Head load, 0.82 ms vs Shoulder load, 0.78 ms). Stride frequency was significantly decreased during load carrying on the head than the load in both hands (46.5 vs 51.7 strides/m). Step, stance, and swing time symmetry were significantly poorer during load carrying on the shoulder than the load in both hands (Step time symmetry ration, 1.10 vs 1.04; Stance time symmetry ratio, 1.11 vs 1.05; Swing time symmetry ratio, 1.11 vs 1.04). The anteroposterior (Shoulder load, 17.47 mm vs Head load, 21.10 mm vs Hand load, −5.10 mm) and mediolateral CoP displacements (Shoulder load, −0.57 mm vs Head load, −1.53 mm vs Hand load, −3.37 ms) significantly increased during load carrying on the shoulder or head compared to a load in both hands. The HR (Head load, 85.2 beats/m vs Shoulder load, 77.5 beats/m vs No load, 69.5 beats/m) and EDA (Hand load, 14.0 µS vs Head load, 14.3 µS vs Shoulder load, 14.1 µS vs No load, 9.0 µS) were significantly larger during load carrying than no load.Research limitations/implicationsThe findings suggest that carrying loads in both hands yields better gait symmetry and dynamic balance than carrying loads on the dominant shoulder or head. Construction managers/instructors should recommend construction workers to carry loads in both hands to improve their gait symmetry and dynamic balance and to lower their risk of falls.Practical implicationsThe potential changes in gait and balance parameters during various load carrying methods will aid the assessment of fall risk in construction workers during loaded walking. Wearable insole sensors that monitor gait and balance in real-time would enable safety managers to identify workers who are at risk of falling during load carriage due to various reasons (e.g. physical exertion, improper carrying techniques, fatigue). Such technology can also empower them to take the necessary steps to prevent falls.Originality/valueThis is the first study to use wearable insole sensors and a photoplethysmography device to assess the impacts of various load carrying approaches on gait parameters, dynamic balance, and physiological measures (i.e. HR and EDA) while walking on stable and unstable terrains.

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Section: Literature Reviewmentioning

confidence: 99%

Effects of load carrying techniques on gait parameters, dynamic balance, and physiological parameters during a manual material handling task

Anwer

Antwi‐Afari

et al. 2021

ECAM

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“…For this purpose, one-way analysis of variance (ANOVA) was used to test whether there was a significant effect of knee type on gait stability. This was followed by a Tukey's post-hoc analysis to detect any significant differences between the groups [28].…”

Section: E Statistical Analysismentioning

confidence: 99%

Development of Linear Regression Models to Estimate the Margin of Stability Based on Spatio-Temporal Gait Parameters

Alamoudı

Alamoudi

2020

IEEE Access

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Spatio-temporal gait parameters such as step width, cadence, stride length, and walking speed contribute to dynamic stability. Several studies have investigated the role of gait parameters in maintaining balance. However, in these studies, subjects were instructed to alter their gait. This intentional alteration has the potential to create error in the results, as subjects are not walking with a natural and comfortable gait. In consideration of this, the sample chosen in this study consisted of patients who had undergone a knee replacement. Such individuals naturally have gait parameters that differ from normal subjects. The primary objective of this study was to develop regression models that predict and measure gait stability in both the anterior-posterior and medio-lateral directions based on gait parameters. The maximum deviation of the extrapolated center of mass from the border of the base of support was the measure of gait stability. A forward stepwise multiple regression analysis was conducted to develop both models. In testing the goodness of fit of models, the values of coefficient of determination, standard error of estimates, and root mean square error were calculated. Both models showed sufficient values of goodness of fit. To improve walking stability and minimize falls, fall-prone people should walk with an adequate base-of-support area, and with lower cadence and speed. The results of this study contribute to an understanding of gait patterns and their relationship to walking stability and to how gait strategies might be taught in physical therapy programs to minimize the risk of falls. INDEX TERMSBase of support, cadence, extrapolated center of mass, gait, stability, step width, stride length, walking speed. RAMI ALAMOUDI received the B.Sc. degree in industrial engineering from King Abdulaziz University, in 2003, and the master's and Ph.D. degrees in industrial engineering from the University of Miami, in 2005 and 2008, respectively. He joined the Department Industrial Engineering, King Abdulaziz University, as an Assistant Professor, where he was promoted to an Associated Professor, in 2015. His research interests include production planning and control, lean manufacturing, simulation and supply chain management. He is currently a member of the Institute of Industrial Engineers (IIE) and the Saudi Society for Systems & Industrial Engineering. MOHAMMED ALAMOUDI received the B.Sc. degree in industrial engineering (IE) from King Abdulaziz University (KAU), Jeddah, Saudi Arabia, in 2011, the M.Sc. and Ph.D. degrees in IE, in 2013 and in 2017, respectively. For his graduate studies, he attended the Industrial Engineering Department, University of Miami (UM), Miami, USA. While his completion of Ph.D. degree, he held the position of a Research Assistant with the Biomechanics Laboratory, UM. He is currently an Assistant Professor with the Department of Industrial Engineering, KAU. His research interests include ergonomics and human factors engineering.

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“…This single walk across the lab was considered an individual 'trial'. Each participant completed 19 trials: These loads were chosen based on prior studies of load position and weight, which similarly studied no load, front load, and symmetric/asymmetric side loads [24,29,31] and used similar weights [24,26,27,32]. Participants were allowed to rest between trials as needed, and the order of trials was randomized between participants.…”

Section: Study Protocolmentioning

confidence: 99%

“…Conversely, carrying behavior has been studied with wearable sensors in significantly less detail. While camera-based studies have shown significant differences between different load locations and weights [ 22 , 23 , 24 , 25 , 26 , 27 , 28 ], to the best of our knowledge only four studies have attempted to automatically classify different load carriage types with wearable sensors, which would be the prerequisite for control of assistive devices. Benocci et al [ 29 ] used four IMUs and a k-nearest-neighbor classifier to differentiate among six backpack positions (all with the same weight): none, on both shoulders, on only left shoulder, on only right shoulder, in right hand, and in left hand; they achieved an overall classification accuracy of 96.7%.…”

Section: Introductionmentioning

confidence: 99%

Load Position and Weight Classification during Carrying Gait Using Wearable Inertial and Electromyographic Sensors

Goršič

Dai

Novak

2020

Sensors

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Lifting and carrying heavy objects is a major aspect of physically intensive jobs. Wearable sensors have previously been used to classify different ways of picking up an object, but have seen only limited use for automatic classification of load position and weight while a person is walking and carrying an object. In this proof-of-concept study, we thus used wearable inertial and electromyographic sensors for offline classification of different load positions (frontal vs. unilateral vs. bilateral side loads) and weights during gait. Ten participants performed 19 different carrying trials each while wearing the sensors, and data from these trials were used to train and evaluate classification algorithms based on supervised machine learning. The algorithms differentiated between frontal and other loads (side/none) with an accuracy of 100%, between frontal vs. unilateral side load vs. bilateral side load with an accuracy of 96.1%, and between different load asymmetry levels with accuracies of 75–79%. While the study is limited by a lack of electromyographic sensors on the arms and a limited number of load positions/weights, it shows that wearable sensors can differentiate between different load positions and weights during gait with high accuracy. In the future, such approaches could be used to control assistive devices or for long-term worker monitoring in physically demanding occupations.

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The effects of different carrying methods on locomotion stability, gait spatio-temporal parameters and spinal stresses

Cited by 28 publications

References 39 publications

Effects of load carrying techniques on gait parameters, dynamic balance, and physiological parameters during a manual material handling task

Effects of load carrying techniques on gait parameters, dynamic balance, and physiological parameters during a manual material handling task

Development of Linear Regression Models to Estimate the Margin of Stability Based on Spatio-Temporal Gait Parameters

Load Position and Weight Classification during Carrying Gait Using Wearable Inertial and Electromyographic Sensors

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