Longitudinal Rollover Strategy as Effective Intervention to Reduce Wrist Injuries During Forward Fall

• Published in: IEEE robotics and automation letters Vol. 3; no. 4; pp. 4187 - 4192
• Main Authors: Abdolshah, Saeed, Rajaei, Nader, Akiyama, Yasuhiro, Yamada, Yoji, Okamoto, Shogo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomechanics; Damage prevention; Elbow; Electromyography; Force; Human detection and tracking; humanoid robots; impact force; Impact loads; Injuries; Injury prevention; longitudinal rollover strategy during a forward fall; Researchers; Robotics; Robots; Rollover; Strategy; Wrist
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2018.2864646

Strategies for reducing fall-related injuries have been assessed through biomechanical studies and employed in robotics. A better understanding of the mechanism of such fall arresting methods can help fall prevention programs to reduce injuries and help robots to prevent damage to themselves. Rollover about the longitudinal axis is an effective strategy to reduce the impact force experienced by the hand and consequently reduce the associated injuries; however, neither biomechanical researchers nor roboticists have studied this useful fall arresting strategy. This study was designed to investigate the impact force experienced by the human hand during a forward fall using the longitudinal rollover strategy. We designed a series of fall experiments in which the subjects were instructed to arrest a forward fall using longitudinal rollover and bimanual strategies. The experimental results showed that during a forward fall, longitudinal rollover considerably reduces the impact force and the risk of injuries. The kinematic data of motion were measured and presented. A comparison of our method with various forward fall arresting methods reported so far in the existing literature showed that our proposed fall arresting strategy is one of the most efficient techniques. Our study results are expected to provide robotics researchers with useful data to design algorithms to reduce robot damage during falls.