The Experts below are selected from a list of 2829 Experts worldwide ranked by ideXlab platform

Maury A. Nussbaum - One of the best experts on this subject based on the ideXlab platform.

  • using a smart textile system for classifying occupational Manual Material Handling tasks evidence from lab based simulations
    Ergonomics, 2019
    Co-Authors: Mohammad Iman Mokhlespour Esfahani, Maury A. Nussbaum, Zhenyu Kong
    Abstract:

    AbstractPhysical monitoring systems represent potentially powerful assessment devices to detect and describe occupational physical activities. A promising technology for such use is smart textile systems (STSs). Our goal in this exploratory study was to assess the feasibility and accuracy of using two STSs to classify several Manual Material Handling (MMH) tasks. Specifically, commercially-available ‘smart’ socks and a custom ‘smart’ shirt were used individually and in combination. Eleven participants simulated nine separate MMH tasks while wearing the STSs, and task classification accuracy was quantified subsequently using several common models. The shirt and socks, both individually and in combination, could classify the simulated tasks with greater than 97% accuracy. Thus, using STSs appears to have potential utility for discriminating occupational physical tasks in the work environment.Practitioner summary: A smart textile system could classify diverse MMH tasks with high accuracy. This technology may...

  • using a smart textile system for classifying occupational Manual Material Handling tasks evidence from lab based simulations
    Ergonomics, 2019
    Co-Authors: Mohammad Iman Mokhlespour Esfahani, Maury A. Nussbaum, Zhenyu Kong
    Abstract:

    AbstractPhysical monitoring systems represent potentially powerful assessment devices to detect and describe occupational physical activities. A promising technology for such use is smart textile s...

  • a method for robust online classification using dictionary learning development and assessment for monitoring Manual Material Handling activities using wearable sensors
    arXiv: Machine Learning, 2018
    Co-Authors: Babak Barazandeh, Mohammadhussein Rafieisakhaei, Maury A. Nussbaum
    Abstract:

    Classification methods based on sparse estimation have drawn much attention recently, due to their effectiveness in processing high-dimensional data such as images. In this paper, a method to improve the performance of a sparse representation classification (SRC) approach is proposed; it is then applied to the problem of online process monitoring of human workers, specifically Manual Material Handling (MMH) operations monitored using wearable sensors (involving 111 sensor channels). Our proposed method optimizes the design matrix (aka dictionary) in the linear model used for SRC, minimizing its ill-posedness to achieve a sparse solution. This procedure is based on the idea of dictionary learning (DL): we optimize the design matrix formed by training datasets to minimize both redundancy and coherency as well as reducing the size of these datasets. Use of such optimized training data can subsequently improve classification accuracy and help decrease the computational time needed for the SRC; it is thus more applicable for online process monitoring. Performance of the proposed methodology is demonstrated using wearable sensor data obtained from Manual Material Handling experiments, and is found to be superior to those of benchmark methods in terms of accuracy, while also requiring computational time appropriate for MMH online monitoring.

  • robust sparse representation based classification using online sensor data for monitoring Manual Material Handling tasks
    IEEE Transactions on Automation Science and Engineering, 2018
    Co-Authors: Babak Barazandeh, Zhenyu Kong, Mohammadhussein Rafieisakhaei, Kaveh Bastani, Maury A. Nussbaum
    Abstract:

    Sensor-based online process monitoring has extensive applications, such as in manufacturing and service industries. In real environments, though, sensor data are often contaminated with noise, leading to severe challenges in accurate data analysis. In the existing literature, noise is generally modeled as Gaussian to analyze sensor data for various applications, for example in fault detection and diagnostics. However, in some applications, such as due to challenging field conditions, sensor data may be disturbed by high levels of outliers such that the Gaussian assumption of sensor noise is inadequate, thus leading to large estimation errors. This paper focuses on online classification applications. A robust sparse representation classification method is proposed, which considers non-Gaussian noise, and thus can effectively analyze sensor data with higher levels of outliers. Case studies were completed, based on both numerically simulated sensor data and actual wearable sensor data from occupational Manual Material Handling process monitoring. The proposed classification method could effectively analyze sensor data with non-Gaussian noise, and outperformed commonly used methods in the literature. Thus, this new method may be advantageous for solving classification problems in challenging field conditions, to address the difficulties of high levels of sensor outliers. Note to Practitioners —This paper proposes a fast, robust classification method for online sensor data classification. The proposed method is designed to cope with high levels of sensor outliers. The robustness of the method and its computational efficiency make it particularly appealing for online sensor data classification in challenging field conditions in which the presence of sensor outliers causes practical difficulties for most existing classification algorithms.

  • age related differences in mechanical demands imposed on the lower back by Manual Material Handling tasks
    Journal of Biomechanics, 2016
    Co-Authors: Iman Shojaei, Maury A. Nussbaum, Milad Vazirian, Emily Croft, Babak Bazrgari
    Abstract:

    The prevalence of low back pain (LBP) increases with age, yet the underlying mechanism(s) responsible for this remains unclear. To explore the role of biomechanical factors, we investigated age-related differences in lower-back biomechanics during sagittally-symmetric simulated Manual Material Handling tasks. For each task, trunk kinematics and mechanical demand on the lower back were examined, from among 60 participants within five equal-sized and gender-balanced age groups spanning from 20 to 70 years old. The tasks involved lowering a 4.5 kg load from an upright standing posture to both knee height and a fixed height and then lifting the load back to the initial upright posture. During these tasks, segmental body kinematics and ground reaction forces were collected using wireless inertial measurement units and a force platform. Overall, older participants completed the tasks with larger pelvic rotation and smaller lumbar flexion. Such adopted trunk kinematics resulted in larger peak shearing demand at the lower back in older vs. younger participants. These results suggest that older individuals may be at a higher risk for developing lower back pain when completing similar Manual Material Handling tasks, consistent with epidemiological evidence for higher risks of occupational low back pain among this cohort.

Eira Viikarijuntura - One of the best experts on this subject based on the ideXlab platform.

  • Manual Material Handling advice and assistive devices for preventing and treating back pain in workers a cochrane systematic review
    Occupational and Environmental Medicine, 2012
    Co-Authors: Jos Verbeek, Karipekka Martimo, Jaro Karppinen, Paul P F M Kuijer, Esapekka Takala, Eira Viikarijuntura
    Abstract:

    In many occupations, it is difficult to avoid imposing heavy loads on the back (eg, lifting and moving patients in healthcare). Therefore, it is not surprising that emphasis has been given to optimising lifting techniques and ways to Manually handle patients and objects to prevent back pain and injuries. More skilled workers are supposed to cope better with adverse ergonomic conditions, resulting in less strain on the back, less back pain and consequently, less back pain-related disability. This has led to a strong belief that it is useful to advise employees or organise training for them on correct Manual Material Handling (MMH) techniques and to provide them with assistive devices. Therefore, we wanted to determine the effectiveness of MMH advice and training and the provision of assistive devices in preventing and treating back pain. We have updated the previous version of the systematic review with a new search, new studies and improved methods.1 2 We searched CENTRAL ( The Cochrane Library 2011, issue 1), MEDLINE, EMBASE, CINAHL, Nioshtic, CISdoc, Science Citation Index and PsychLIT to February 2011. We included randomised controlled trials (RCT) and, because we thought it would be difficult to find RCTs, cohort studies with a …

  • Manual Material Handling advice and assistive devices for preventing and treating back pain in workers
    Cochrane Database of Systematic Reviews, 2011
    Co-Authors: Jos Verbeek, Karipekka Martimo, Jaro Karppinen, Paul P F M Kuijer, Eira Viikarijuntura, Esapekka Takala
    Abstract:

    BACKGROUND: Training and assistive devices are considered major interventions to prevent back pain among workers exposed to Manual Material Handling (MMH). OBJECTIVES: To determine the effectiveness of MMH advice and training and the provision of assistive devices in preventing and treating back pain. SEARCH STRATEGY: We searched MEDLINE to November 2005, EMBASE to August 2005, and CENTRAL, the Back Group's Trials Register, CINAHL, Nioshtic, CISdoc, Science Citation Index, and PsychLIT to September 2005. SELECTION CRITERIA: We included randomized controlled trials (RCT) and cohort studies with a concurrent control group, aimed at changing human behaviour in MMH and measuring back pain, back pain-related disability or sickness absence. DATA COLLECTION AND ANALYSIS: Two authors independently extracted the data and assessed the methodological quality using the criteria recommended by the Back Review Group for RCTs and MINORS for the cohort studies. One author of an original study supplied additional data for the review.The results and conclusions are based on the primary analysis of RCTs. We conducted a secondary analysis with cohort studies. We compared and contrasted the conclusions from the primary and secondary analyses. MAIN RESULTS: We included six RCTs (17,720 employees) and five cohort studies (772 employees). All studies focused on prevention of back pain. Two RCTs and all cohort studies met the majority of the quality criteria and were labeled high quality.We summarized the strength of the evidence with a qualitative analysis since the lack of data precluded a statistical analysis.There is moderate evidence that MMH advice and training are no more effective at preventing back pain or back pain-related disability than no intervention (four studies) or minor advice (one study). There is limited evidence that MMH advice and training are no more effective than physical exercise or back belt use in preventing back pain (three studies), and that MMH advice plus assistive devices are not more effective than MMH advice alone (one study) or no intervention (one study) in preventing back pain or related disability.The results of the cohort studies were similar to the randomised studies. AUTHORS' CONCLUSIONS: There is limited to moderate evidence that MMH advice and training with or without assistive devices do not prevent back pain, back pain-related disability or reduce sick leave when compared to no intervention or alternative interventions. There is no evidence available for the effectiveness of MMH advice and training or MMH assistive devices for treating back pain

Esapekka Takala - One of the best experts on this subject based on the ideXlab platform.

  • Manual Material Handling advice and assistive devices for preventing and treating back pain in workers a cochrane systematic review
    Occupational and Environmental Medicine, 2012
    Co-Authors: Jos Verbeek, Karipekka Martimo, Jaro Karppinen, Paul P F M Kuijer, Esapekka Takala, Eira Viikarijuntura
    Abstract:

    In many occupations, it is difficult to avoid imposing heavy loads on the back (eg, lifting and moving patients in healthcare). Therefore, it is not surprising that emphasis has been given to optimising lifting techniques and ways to Manually handle patients and objects to prevent back pain and injuries. More skilled workers are supposed to cope better with adverse ergonomic conditions, resulting in less strain on the back, less back pain and consequently, less back pain-related disability. This has led to a strong belief that it is useful to advise employees or organise training for them on correct Manual Material Handling (MMH) techniques and to provide them with assistive devices. Therefore, we wanted to determine the effectiveness of MMH advice and training and the provision of assistive devices in preventing and treating back pain. We have updated the previous version of the systematic review with a new search, new studies and improved methods.1 2 We searched CENTRAL ( The Cochrane Library 2011, issue 1), MEDLINE, EMBASE, CINAHL, Nioshtic, CISdoc, Science Citation Index and PsychLIT to February 2011. We included randomised controlled trials (RCT) and, because we thought it would be difficult to find RCTs, cohort studies with a …

  • Manual Material Handling advice and assistive devices for preventing and treating back pain in workers
    Cochrane Database of Systematic Reviews, 2011
    Co-Authors: Jos Verbeek, Karipekka Martimo, Jaro Karppinen, Paul P F M Kuijer, Eira Viikarijuntura, Esapekka Takala
    Abstract:

    BACKGROUND: Training and assistive devices are considered major interventions to prevent back pain among workers exposed to Manual Material Handling (MMH). OBJECTIVES: To determine the effectiveness of MMH advice and training and the provision of assistive devices in preventing and treating back pain. SEARCH STRATEGY: We searched MEDLINE to November 2005, EMBASE to August 2005, and CENTRAL, the Back Group's Trials Register, CINAHL, Nioshtic, CISdoc, Science Citation Index, and PsychLIT to September 2005. SELECTION CRITERIA: We included randomized controlled trials (RCT) and cohort studies with a concurrent control group, aimed at changing human behaviour in MMH and measuring back pain, back pain-related disability or sickness absence. DATA COLLECTION AND ANALYSIS: Two authors independently extracted the data and assessed the methodological quality using the criteria recommended by the Back Review Group for RCTs and MINORS for the cohort studies. One author of an original study supplied additional data for the review.The results and conclusions are based on the primary analysis of RCTs. We conducted a secondary analysis with cohort studies. We compared and contrasted the conclusions from the primary and secondary analyses. MAIN RESULTS: We included six RCTs (17,720 employees) and five cohort studies (772 employees). All studies focused on prevention of back pain. Two RCTs and all cohort studies met the majority of the quality criteria and were labeled high quality.We summarized the strength of the evidence with a qualitative analysis since the lack of data precluded a statistical analysis.There is moderate evidence that MMH advice and training are no more effective at preventing back pain or back pain-related disability than no intervention (four studies) or minor advice (one study). There is limited evidence that MMH advice and training are no more effective than physical exercise or back belt use in preventing back pain (three studies), and that MMH advice plus assistive devices are not more effective than MMH advice alone (one study) or no intervention (one study) in preventing back pain or related disability.The results of the cohort studies were similar to the randomised studies. AUTHORS' CONCLUSIONS: There is limited to moderate evidence that MMH advice and training with or without assistive devices do not prevent back pain, back pain-related disability or reduce sick leave when compared to no intervention or alternative interventions. There is no evidence available for the effectiveness of MMH advice and training or MMH assistive devices for treating back pain

Zhenyu Kong - One of the best experts on this subject based on the ideXlab platform.

  • using a smart textile system for classifying occupational Manual Material Handling tasks evidence from lab based simulations
    Ergonomics, 2019
    Co-Authors: Mohammad Iman Mokhlespour Esfahani, Maury A. Nussbaum, Zhenyu Kong
    Abstract:

    AbstractPhysical monitoring systems represent potentially powerful assessment devices to detect and describe occupational physical activities. A promising technology for such use is smart textile systems (STSs). Our goal in this exploratory study was to assess the feasibility and accuracy of using two STSs to classify several Manual Material Handling (MMH) tasks. Specifically, commercially-available ‘smart’ socks and a custom ‘smart’ shirt were used individually and in combination. Eleven participants simulated nine separate MMH tasks while wearing the STSs, and task classification accuracy was quantified subsequently using several common models. The shirt and socks, both individually and in combination, could classify the simulated tasks with greater than 97% accuracy. Thus, using STSs appears to have potential utility for discriminating occupational physical tasks in the work environment.Practitioner summary: A smart textile system could classify diverse MMH tasks with high accuracy. This technology may...

  • using a smart textile system for classifying occupational Manual Material Handling tasks evidence from lab based simulations
    Ergonomics, 2019
    Co-Authors: Mohammad Iman Mokhlespour Esfahani, Maury A. Nussbaum, Zhenyu Kong
    Abstract:

    AbstractPhysical monitoring systems represent potentially powerful assessment devices to detect and describe occupational physical activities. A promising technology for such use is smart textile s...

  • robust sparse representation based classification using online sensor data for monitoring Manual Material Handling tasks
    IEEE Transactions on Automation Science and Engineering, 2018
    Co-Authors: Babak Barazandeh, Zhenyu Kong, Mohammadhussein Rafieisakhaei, Kaveh Bastani, Maury A. Nussbaum
    Abstract:

    Sensor-based online process monitoring has extensive applications, such as in manufacturing and service industries. In real environments, though, sensor data are often contaminated with noise, leading to severe challenges in accurate data analysis. In the existing literature, noise is generally modeled as Gaussian to analyze sensor data for various applications, for example in fault detection and diagnostics. However, in some applications, such as due to challenging field conditions, sensor data may be disturbed by high levels of outliers such that the Gaussian assumption of sensor noise is inadequate, thus leading to large estimation errors. This paper focuses on online classification applications. A robust sparse representation classification method is proposed, which considers non-Gaussian noise, and thus can effectively analyze sensor data with higher levels of outliers. Case studies were completed, based on both numerically simulated sensor data and actual wearable sensor data from occupational Manual Material Handling process monitoring. The proposed classification method could effectively analyze sensor data with non-Gaussian noise, and outperformed commonly used methods in the literature. Thus, this new method may be advantageous for solving classification problems in challenging field conditions, to address the difficulties of high levels of sensor outliers. Note to Practitioners —This paper proposes a fast, robust classification method for online sensor data classification. The proposed method is designed to cope with high levels of sensor outliers. The robustness of the method and its computational efficiency make it particularly appealing for online sensor data classification in challenging field conditions in which the presence of sensor outliers causes practical difficulties for most existing classification algorithms.

Jos Verbeek - One of the best experts on this subject based on the ideXlab platform.

  • Manual Material Handling advice and assistive devices for preventing and treating back pain in workers a cochrane systematic review
    Occupational and Environmental Medicine, 2012
    Co-Authors: Jos Verbeek, Karipekka Martimo, Jaro Karppinen, Paul P F M Kuijer, Esapekka Takala, Eira Viikarijuntura
    Abstract:

    In many occupations, it is difficult to avoid imposing heavy loads on the back (eg, lifting and moving patients in healthcare). Therefore, it is not surprising that emphasis has been given to optimising lifting techniques and ways to Manually handle patients and objects to prevent back pain and injuries. More skilled workers are supposed to cope better with adverse ergonomic conditions, resulting in less strain on the back, less back pain and consequently, less back pain-related disability. This has led to a strong belief that it is useful to advise employees or organise training for them on correct Manual Material Handling (MMH) techniques and to provide them with assistive devices. Therefore, we wanted to determine the effectiveness of MMH advice and training and the provision of assistive devices in preventing and treating back pain. We have updated the previous version of the systematic review with a new search, new studies and improved methods.1 2 We searched CENTRAL ( The Cochrane Library 2011, issue 1), MEDLINE, EMBASE, CINAHL, Nioshtic, CISdoc, Science Citation Index and PsychLIT to February 2011. We included randomised controlled trials (RCT) and, because we thought it would be difficult to find RCTs, cohort studies with a …

  • Manual Material Handling advice and assistive devices for preventing and treating back pain in workers
    Cochrane Database of Systematic Reviews, 2011
    Co-Authors: Jos Verbeek, Karipekka Martimo, Jaro Karppinen, Paul P F M Kuijer, Eira Viikarijuntura, Esapekka Takala
    Abstract:

    BACKGROUND: Training and assistive devices are considered major interventions to prevent back pain among workers exposed to Manual Material Handling (MMH). OBJECTIVES: To determine the effectiveness of MMH advice and training and the provision of assistive devices in preventing and treating back pain. SEARCH STRATEGY: We searched MEDLINE to November 2005, EMBASE to August 2005, and CENTRAL, the Back Group's Trials Register, CINAHL, Nioshtic, CISdoc, Science Citation Index, and PsychLIT to September 2005. SELECTION CRITERIA: We included randomized controlled trials (RCT) and cohort studies with a concurrent control group, aimed at changing human behaviour in MMH and measuring back pain, back pain-related disability or sickness absence. DATA COLLECTION AND ANALYSIS: Two authors independently extracted the data and assessed the methodological quality using the criteria recommended by the Back Review Group for RCTs and MINORS for the cohort studies. One author of an original study supplied additional data for the review.The results and conclusions are based on the primary analysis of RCTs. We conducted a secondary analysis with cohort studies. We compared and contrasted the conclusions from the primary and secondary analyses. MAIN RESULTS: We included six RCTs (17,720 employees) and five cohort studies (772 employees). All studies focused on prevention of back pain. Two RCTs and all cohort studies met the majority of the quality criteria and were labeled high quality.We summarized the strength of the evidence with a qualitative analysis since the lack of data precluded a statistical analysis.There is moderate evidence that MMH advice and training are no more effective at preventing back pain or back pain-related disability than no intervention (four studies) or minor advice (one study). There is limited evidence that MMH advice and training are no more effective than physical exercise or back belt use in preventing back pain (three studies), and that MMH advice plus assistive devices are not more effective than MMH advice alone (one study) or no intervention (one study) in preventing back pain or related disability.The results of the cohort studies were similar to the randomised studies. AUTHORS' CONCLUSIONS: There is limited to moderate evidence that MMH advice and training with or without assistive devices do not prevent back pain, back pain-related disability or reduce sick leave when compared to no intervention or alternative interventions. There is no evidence available for the effectiveness of MMH advice and training or MMH assistive devices for treating back pain