2011
DOI: 10.1007/978-3-642-21666-4_17
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Detection of Wheelchair User Activities Using Wearable Sensors

Abstract: Wearable sensors are increasingly used to monitor and quantify physical activity types and levels in a real-life environment. In this project we studied the activity classification in manual wheelchair users using wearable sensors. Twenty-seven subjects performed a series of representative activities of daily living in a semi-structured setting with a wheelchair propulsion monitoring device (WPMD) attached to their upper limb and their wheelchair. The WPMD included a wheel rotation datalogger that collected wh… Show more

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Cited by 13 publications
(8 citation statements)
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“…However, the fact that other types of interaction (e.g. ADLs [5] or wheelchair stroke patterns [6]) can be detected using Human Activity Recognition methods suggests that a semi-automated approach could be effective, by detecting where a user might be encountering a problem and then inviting an annotation from the user.…”
Section: Automated (Or Semi-automated)mentioning
confidence: 99%
“…However, the fact that other types of interaction (e.g. ADLs [5] or wheelchair stroke patterns [6]) can be detected using Human Activity Recognition methods suggests that a semi-automated approach could be effective, by detecting where a user might be encountering a problem and then inviting an annotation from the user.…”
Section: Automated (Or Semi-automated)mentioning
confidence: 99%
“…Wearable sensors such as inertial measurement units (IMUs) and electromyography (EMG) sensors are minimally intrusive measurement tools that can be used to quantify movements [ 37 , 38 ] and muscle activity over a longer period. The largest limitation of using IMUs lies in “integration drift” when fusing sensor signals into orientation estimates [ 39 ] and the temperature depending bias of gyroscopes has been reduced to a minimum in the more recent generations of sensors.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, the use of only raw (or calibrated) wearable sensor data such as the acceleration and angular velocity for the classification of activities is a growing field of research, with promising results. Especially in the able-bodied population, IMUs or embedded sensors (e.g., smartphones, smartwatches) have been shown to be a valuable tool to monitor activities in a free-living environment [ 40 , 41 , 42 ], but only a few studies have investigated activity detection and classification among MWUs [ 37 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ]. Previous research examining the use of wearable sensors has predominantly focused on physical activity detection to estimate the activity levels and energy expenditure in MWUs with SCI [ 50 , 51 , 52 , 53 , 54 , 55 ].…”
Section: Introductionmentioning
confidence: 99%
“…Also using the wheel rotation monitor to identify self-propulsion episodes may not be accurate in real-life settings. Our previous study showed that the monitors we used here were able to detect self-propulsion, external pushing, sedentary activities, and other activities with an accuracy of 90% using a laboratory-based protocol [ 37 ]. Future testing should consider real-life testing with a mixture of wheelchair propulsion and other activities of daily living in the home and community settings and combine the detection of wheelchair episodes with the estimation of propulsion parameters when assessing the overall estimation accuracies of temporal propulsion parameters.…”
Section: Discussionmentioning
confidence: 99%