A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks
暂无分享,去创建一个
M. Cesarelli | E. Capodaglio | M. Panigazzi | G. Cesarelli | L. Donisi | Francesco Amato | G. D'addio
[1] M. Cesarelli,et al. Feasibility of Tree-based Machine Learning algorithms fed with surface electromyographic features to discriminate risk classes according to NIOSH , 2022, 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA).
[2] G. D’Addio,et al. Bidimensional and Tridimensional Poincaré Maps in Cardiology: A Multiclass Machine Learning Study , 2022, Electronics.
[3] G. D’Addio,et al. Extracting Features from Poincaré Plots to Distinguish Congestive Heart Failure Patients According to NYHA Classes , 2021, Bioengineering.
[4] Reza Akhavian,et al. Automated Workers Ergonomic Risk Assessment in Manual Material Handling using sEMG Wearable Sensors and Machine Learning , 2021, Electronics.
[5] Allison L. Clouthier,et al. The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review. , 2021, Applied ergonomics.
[6] V. Provitera,et al. Positive impact of short-term gait rehabilitation in Parkinson patients: a combined approach based on statistics and machine learning. , 2021, Mathematical biosciences and engineering : MBE.
[7] Gianni D'Addio,et al. Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning , 2021, Sensors.
[8] Elena Stefana,et al. Wearable Devices for Ergonomics: A Systematic Literature Review , 2021, Sensors.
[9] M. Cesarelli,et al. Machine learning to predict mortality after rehabilitation among patients with severe stroke , 2020, Scientific Reports.
[10] Sobhan Sarkar,et al. Machine learning in occupational accident analysis: A review using science mapping approach with citation network analysis , 2020 .
[11] G. Pagano,et al. Benchmarking between two wearable inertial systems for gait analysis based on a different sensor placement using several statistical approaches , 2020 .
[12] Gianni D'Addio,et al. Rehabilitation Outcome in Patients undergone Hip or Knee Replacement Surgery using Inertial Technology for Gait Analysis , 2020, 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA).
[13] Bernardo Lanzillo,et al. Repeatability of Spatio-Temporal Gait Measurements in Parkinson’s Disease , 2020, 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA).
[14] C. Zampieri,et al. Normative database of spatiotemporal gait parameters using inertial sensors in typically developing children and young adults. , 2020, Gait & posture.
[15] Reuben F. Burch,et al. Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics , 2020, International journal of environmental research and public health.
[16] Silvia Conforto,et al. Lifting Activity Assessment Using Kinematic Features and Neural Networks , 2020, Applied Sciences.
[17] Clive D’Souza,et al. A Narrative Review on Contemporary and Emerging Uses of Inertial Sensing in Occupational Ergonomics. , 2020, International journal of industrial ergonomics.
[18] Zaccaria Del Prete,et al. Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach , 2020, Sensors.
[19] M. Mancini,et al. Validity of Mobility Lab (version 2) for gait assessment in young adults, older adults and Parkinson’s disease , 2019, Physiological measurement.
[20] Liang Ma,et al. Continuous Measurement of Muscle Fatigue Using Wearable Sensors During Light Manual Operations , 2019, HCI.
[21] G. Nassis,et al. Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review , 2019, Sports Medicine - Open.
[22] Gianni D'Addio,et al. Agreement between Opal and G-Walk Wearable Inertial Systems in Gait Analysis on Normal and Pathological Subjects , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[23] Silvia Conforto,et al. Surface electromyography for risk assessment in work activities designed using the “revised NIOSH lifting equation” , 2018, International Journal of Industrial Ergonomics.
[24] Tiwana Varrecchia,et al. Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges—A Systematic Review , 2018, International journal of environmental research and public health.
[25] Ehsan Nabovati,et al. Fuzzy decision support systems to diagnose musculoskeletal disorders: A systematic literature review , 2018, Comput. Methods Programs Biomed..
[26] Maarten van Smeden,et al. Sample size for binary logistic prediction models: Beyond events per variable criteria , 2018, Statistical methods in medical research.
[27] Silvia Conforto,et al. Lifting activity assessment using surface electromyographic features and neural networks , 2018, International Journal of Industrial Ergonomics.
[28] Jari Porras,et al. Tapping into the wearable device revolution in the work environment: a systematic review , 2018, Inf. Technol. People.
[29] Pascal Madeleine,et al. Accuracy of identification of low or high risk lifting during standardised lifting situations , 2018, Ergonomics.
[30] Richard F. Sesek,et al. Barriers to the Adoption of Wearable Sensors in the Workplace: A Survey of Occupational Safety and Health Professionals , 2018, Hum. Factors.
[31] Xin Fang,et al. Reference values of gait using APDM movement monitoring inertial sensor system , 2018, Royal Society Open Science.
[32] Martina Minnerop,et al. Accuracy and repeatability of two methods of gait analysis - GaitRite™ und Mobility Lab™ - in subjects with cerebellar ataxia. , 2016, Gait & posture.
[33] Fay B Horak,et al. Potential of APDM mobility lab for the monitoring of the progression of Parkinson’s disease , 2016, Expert review of medical devices.
[34] Kirsten Vallmuur,et al. Machine learning approaches to analysing textual injury surveillance data: a systematic review. , 2015, Accident; analysis and prevention.
[35] Stephen Bao,et al. Automation of Workplace Lifting Hazard Assessment for Musculoskeletal Injury Prevention , 2014, Annals of Occupational and Environmental Medicine.
[36] Thomas R. Waters,et al. Efficacy of the Revised NIOSH Lifting Equation to Predict Risk of Low-Back Pain Associated With Manual Lifting , 2014, Hum. Factors.
[37] T. Waters,et al. Efficacy of the Revised NIOSH Lifting Equation to Predict Risk of Low Back Pain Due to Manual Lifting: Expanded Cross-Sectional Analysis , 2011, Journal of occupational and environmental medicine.
[38] E. Vieira,et al. Risk factors for work-related musculoskeletal disorders: A systematic review of recent longitudinal studies. , 2009, American journal of industrial medicine.
[39] H. Hashizume,et al. Questions to Improve Quality of Life with Wearables: Humans, Technology, and the World , 2006, 2006 International Conference on Hybrid Information Technology.
[40] O. Menoni,et al. MAPO index for risk assessment of patient manual handling in hospital wards: a validation study , 2006, Ergonomics.
[41] S. Hignett,et al. Rapid entire body assessment (REBA). , 2000, Applied ergonomics.
[42] T. Waters,et al. Evaluation of the revised NIOSH lifting equation. A cross-sectional epidemiologic study. , 1999, Spine.
[43] B. Bernard,et al. Musculoskeletal disorders and workplace factors: a critical review of epidemiologic evidence for work-related musculoskeletal disorders of the neck, upper extremity, and low back , 1997 .
[44] T R Hales,et al. Epidemiology of work-related musculoskeletal disorders. , 1996, The Orthopedic clinics of North America.
[45] S E Mathiassen,et al. Assessment of physical work load in epidemiologic studies: concepts, issues and operational considerations. , 1994, Ergonomics.
[46] A Garg,et al. Revised NIOSH equation for the design and evaluation of manual lifting tasks. , 1993, Ergonomics.
[47] L McAtamney,et al. RULA: a survey method for the investigation of work-related upper limb disorders. , 1993, Applied ergonomics.
[48] James McNames,et al. Mobility Lab to Assess Balance and Gait with Synchronized Body-worn Sensors. , 2011, Journal of bioengineering & biomedical science.
[49] 임종호,et al. Revised NIOSH Lifting Equation의 현장 적용 , 1995 .
[50] W. Press,et al. Savitzky-Golay Smoothing Filters , 2022 .