Prevalence and risk factor analysis of lower extremity abnormal alignment characteristics among rice farmers

BACKGROUND: Rice farming activities involve prolonged manual work and human-machine interaction. Prolonged farming risk-exposure may result in lower limb malalignment. This malalignment may increase the risk of lower extremity injury and physical disabilities. However, the prevalence and factors associated with lower extremity malalignment have not yet been reported. This study aimed to investigate the prevalence and risk factors of lower extremity malalignment among rice farmers. METHODS: A cross-sectional survey was conducted with 249 rice farmers. Lower extremity alignment assessment included: pelvic tilt angle, limb length equality, femoral torsion, quadriceps (Q) angle, tibiofemoral angle, genu recurvatum, rearfoot angle, and medial longitudinal arch angle. Descriptive statistics were used to analyze participant characteristics and prevalence of lower extremity malalignment. Logistic regression analysis was used to identify risk factors. RESULTS: The highest prevalence of lower extremity malalignment was foot pronation (36.14%), followed by the abnormal Q angle (34.94%), tibiofemoral angle (31.73%), pelvic tilt angle (30.52%), femoral antetorsion (28.11%), limb length inequality (22.49%), tibial torsion (21.29%), and genu recurvatum (11.24%). In females, the risk factors were abnormal Q angle, tibiofemoral angle, and genu recurvatum. Being overweight was a risk factor for abnormal pelvic tilt angle, Q angle, and tibiofemoral angle. Age was a risk factor for limb length inequality. Years of farming were a major risk factor for abnormal Q angle, tibiofemoral angle, and foot malalignment. CONCLUSION: Prevalence of lower extremity malalignment was reported in this study. Female sex, being overweight, and years of farming were major risk factors for lower extremity malalignment. Lower extremity screening should assist in the identification of foot and knee malalignment in rice farmers. This may then lead to early prevention of musculoskeletal disorders arising from such malalignment.Usa Karukunchit, Rungthip Puntumetakul, Manida Swangnetr, Rose Boucau

Prevalence and risk factor analysis of lower extremity abnormal alignment characteristics among rice farmers

Usa Karukunchit,1,2 Rungthip Puntumetakul,1,3 Manida Swangnetr,1,4 Rose Boucaut5 1Research Center in Back, Neck, Other Joint Pain and Human Performance (BNOJPH), 2Faculty of Associated Medical Sciences, Khon Kaen University, 3School of Physical Therapy, Faculty of Associated Medical Sciences, Khon Kaen University, 4Department of Production Technology, Faculty of Technology, Khon Kaen University, Khon Kaen, Thailand; 5School of Health Sciences (Physiotherapy), iCAHE (International Centre for Allied Health Evidence), Sansom Institute for Health Research, University of South Australia, Adelaide, SA, Australia Background: Rice farming activities involve prolonged manual work and human–machine interaction. Prolonged farming risk-exposure may result in lower limb malalignment. This malalignment may increase the risk of lower extremity injury and physical disabilities. However, the prevalence and factors associated with lower extremity malalignment have not yet been reported. This study aimed to investigate the prevalence and risk factors of lower extremity malalignment among rice farmers.Methods: A cross-sectional survey was conducted with 249 rice farmers. Lower extremity alignment assessment included: pelvic tilt angle, limb length equality, femoral torsion, quadriceps (Q) angle, tibiofemoral angle, genu recurvatum, rearfoot angle, and medial longitudinal arch angle. Descriptive statistics were used to analyze participant characteristics and prevalence of lower extremity malalignment. Logistic regression analysis was used to identify risk factors.Results: The highest prevalence of lower extremity malalignment was foot pronation (36.14%), followed by the abnormal Q angle (34.94%), tibiofemoral angle (31.73%), pelvic tilt angle (30.52%), femoral antetorsion (28.11%), limb length inequality (22.49%), tibial torsion (21.29%), and genu recurvatum (11.24%). In females, the risk factors were abnormal Q angle, tibiofemoral angle, and genu recurvatum. Being overweight was a risk factor for abnormal pelvic tilt angle, Q angle, and tibiofemoral angle. Age was a risk factor for limb length inequality. Years of farming were a major risk factor for abnormal Q angle, tibiofemoral angle, and foot malalignment.Conclusion: Prevalence of lower extremity malalignment was reported in this study. Female sex, being overweight, and years of farming were major risk factors for lower extremity mal­alignment. Lower extremity screening should assist in the identification of foot and knee mal­alignment in rice farmers. This may then lead to early prevention of musculoskeletal disorders arising from such malalignment. Keywords: lower extremity malalignment, prevalence, rice farmer, risk factors&nbsp

An investigation of responses to robot-initiated touch in a nursing context

© The Author(s) 2013. This article is published with open access at Springerlink.comDOI: 10.1007/s12369-013-0215-xPhysical human-robot interaction has the potential to be useful in a number of domains, but this will depend on how people respond to the robot’s actions. For some domains, such as healthcare, a robot is likely to initiate physical contact with a person’s body. In order to investigate how people respond to this type of interaction, we conducted an experiment with 56 people in which a robotic nurse autonomously touched and wiped each participant’s forearm. On average, participants had a favorable response to the first time the robot touched them. However, we found that the perceived intent of the robot significantly influenced people’s responses. If people believed that the robot intended to clean their arms, the participants tended to respond more favorably than if they believed the robot intended to comfort them, even though the robot’s manipulation behavior was the same. Our results suggest that roboticists should consider this social factor in addition to the mechanics of physical interaction. Surprisingly, we found that participants in our study responded less favorably when given a verbal warning prior to the robot’s actions. In addition to these main results, we present post-hoc analyses of participants’ galvanic skin responses (GSR), open-ended responses, attitudes towards robots, and responses to a second trial

Emotional states detection approaches based on physiological signals for healthcare applications: A review

Mood disorders, anxiety, depression, and stress affect people’s quality of life and increase the vulnerability to diseases and infections. Depression, e.g., can carry undesirable consequences such as death. Hence, emotional states detection approaches using wearable technology are gaining interest in the last few years. Emerging wearable devices allow monitoring different physiological signals in order to extract useful information about people’s health status and provide feedback about their health condition. Wearable applications include e.g., patient monitoring, stress detection, fitness monitoring, wellness monitoring, and assisted living for elderly people, to name a few. This increased interests in wearable applications have allowed the development of new approaches to assist people in everyday activities and emergencies that can be incorporated into the smart city concept. Accurate emotional state detection approaches will allow an effective assistance, thus improving people’s quality of life and well-being. With these issues in mind, this chapter discusses existing emotional states’ approaches using machine and/or deep learning techniques, the most commonly used physiological signals in these approaches, existing physiological databases for emotion recognition, and highlights challenges and future research directions in this field. © Springer Nature Switzerland AG 2020