{"id":4837,"date":"2017-01-14T13:00:33","date_gmt":"2017-01-14T18:00:33","guid":{"rendered":"https:\/\/Engineering.Purdue.edu\/cdesign\/wp\/?p=4837"},"modified":"2021-08-10T15:29:29","modified_gmt":"2021-08-10T20:29:29","slug":"modeling-cumulative-arm-fatigue","status":"publish","type":"post","link":"https:\/\/engineering.purdue.edu\/cdesign\/wp\/modeling-cumulative-arm-fatigue\/","title":{"rendered":"Modeling Cumulative Arm Fatigue in Mid-Air Interaction based on Perceived Exertion and Kinetics of Arm Motion"},"content":{"rendered":"
\"CumulativeFatigue_CHI17\"<\/a>

Estimating subjective fatigue based on cumulative fatigue model and biomechanical upper limb\u00c2\u00a0analysis in mid-air interaction. Left: sketches of mid-air pointing tasks performed in the experiments. Middle: biomechanical model of the upper limb and three-compartment muscle (TCM) model. Right: results of the leave-one-out cross-validation of the TCM model across all subject data (green-upward\/downward triangles: upper\/lower bound of ground-truth, blue circles: averaged ground-truth, black crosses: TCM estimates, red circles: averaged TCM estimages, orange\/purple circles: averaged existing fatigue metric).<\/p><\/div>\n

Quantifying cumulative arm muscle fatigue is a critical factor in understanding, evaluating, and optimizing user experience during prolonged mid-air interaction. A reasonably accurate estimation of fatigue requires an estimate of an individual’s strength. However, there is no easy-to-access method to measure individual strength to accommodate inter-individual differences. Furthermore, fatigue is influenced by both psychological and physiological factors, but no current HCI model provides good estimates of cumulative subjective fatigue. We present a new, simple method to estimate the maximum shoulder torque through a mid-air pointing task, which agrees with direct strength measurements. We then introduce a cumulative fatigue model informed by subjective and biomechanical measures. We evaluate the performance of the model in estimating cumulative subjective fatigue in mid-air interaction by performing multiple cross-validations and a comparison with an existing fatigue metric. Finally, we discuss the potential of our approach for real-time evaluation of subjective fatigue as well as future challenges.<\/p>\n

Downloads:<\/strong><\/span>
\nWe released the fatigue model implementation and the biomechanical upper limb analysis [GitHub Link<\/a>].<\/p>\n","protected":false},"excerpt":{"rendered":"

Quantifying cumulative arm muscle fatigue is a critical factor in understanding, evaluating, and optimizing user experience during prolonged mid-air interaction. A reasonably accurate estimation of fatigue requires an estimate of an individual’s strength. However, there is no easy-to-access method to measure individual strength to accommodate inter-individual differences. Furthermore, fatigue is influenced by both psychological and […]<\/p>\n","protected":false},"author":18,"featured_media":4863,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","_links_to":"","_links_to_target":""},"categories":[363,8,284,3,268,285,228],"tags":[],"yoast_head":"\nModeling Cumulative Arm Fatigue in Mid-Air Interaction based on Perceived Exertion and Kinetics of Arm Motion - Convergence Design Lab, Purdue University<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.purdue.edu\/cdesign\/wp\/modeling-cumulative-arm-fatigue\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Modeling Cumulative Arm Fatigue in Mid-Air Interaction based on Perceived Exertion and Kinetics of Arm Motion - Convergence Design Lab, Purdue University\" \/>\n<meta property=\"og:description\" content=\"Quantifying cumulative arm muscle fatigue is a critical factor in understanding, evaluating, and optimizing user experience during prolonged mid-air interaction. A reasonably accurate estimation of fatigue requires an estimate of an individual’s strength. However, there is no easy-to-access method to measure individual strength to accommodate inter-individual differences. 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