Abstract
To provide assistance with an active exoskeleton, the control system of the device has to automatically detect the onset of the user’s movement and provide timely assistance, according to the recognized movement. In this paper, we present an algorithm designed to detect the lift movement with an active pelvis exoskeleton, based on a quadratic-discriminant-analysis classifier combined with a rule-based algorithm. The algorithm relies on sensory information acquired from the sensory apparatus of the exoskeleton, without needing additional sensors to be placed on the user’s body. The algorithm was validated in experiments with seven healthy subjects. Participants were requested to execute different actions, i.e. lift and lower a load, stand up, sit down and walk, while wearing the exoskeleton. On average, the algorithm showed an accuracy of 98.7 ± 0.6% in recognizing the lift task; such performance make it suitable for use in real application scenarios.
| Original language | English |
|---|---|
| Title of host publication | Biosystems and Biorobotics |
| Publisher | Springer International Publishing AG |
| Pages | 224-228 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - Jan 1 2019 |
Publication series
| Name | Biosystems and Biorobotics |
|---|---|
| Volume | 22 |
| ISSN (Print) | 2195-3562 |
| ISSN (Electronic) | 2195-3570 |
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ASJC Scopus subject areas
- Biomedical Engineering
- Mechanical Engineering
- Artificial Intelligence
Cite this
Lift movement detection with a QDA classifier for an active hip exoskeleton. / Chen, Baojun; Grazi, Lorenzo; Lanotte, Francesco; Vitiello, Nicola; Crea, Simona.
Biosystems and Biorobotics. Springer International Publishing AG, 2019. p. 224-228 (Biosystems and Biorobotics; Vol. 22).Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Lift movement detection with a QDA classifier for an active hip exoskeleton
AU - Chen, Baojun
AU - Grazi, Lorenzo
AU - Lanotte, Francesco
AU - Vitiello, Nicola
AU - Crea, Simona
PY - 2019/1/1
Y1 - 2019/1/1
N2 - To provide assistance with an active exoskeleton, the control system of the device has to automatically detect the onset of the user’s movement and provide timely assistance, according to the recognized movement. In this paper, we present an algorithm designed to detect the lift movement with an active pelvis exoskeleton, based on a quadratic-discriminant-analysis classifier combined with a rule-based algorithm. The algorithm relies on sensory information acquired from the sensory apparatus of the exoskeleton, without needing additional sensors to be placed on the user’s body. The algorithm was validated in experiments with seven healthy subjects. Participants were requested to execute different actions, i.e. lift and lower a load, stand up, sit down and walk, while wearing the exoskeleton. On average, the algorithm showed an accuracy of 98.7 ± 0.6% in recognizing the lift task; such performance make it suitable for use in real application scenarios.
AB - To provide assistance with an active exoskeleton, the control system of the device has to automatically detect the onset of the user’s movement and provide timely assistance, according to the recognized movement. In this paper, we present an algorithm designed to detect the lift movement with an active pelvis exoskeleton, based on a quadratic-discriminant-analysis classifier combined with a rule-based algorithm. The algorithm relies on sensory information acquired from the sensory apparatus of the exoskeleton, without needing additional sensors to be placed on the user’s body. The algorithm was validated in experiments with seven healthy subjects. Participants were requested to execute different actions, i.e. lift and lower a load, stand up, sit down and walk, while wearing the exoskeleton. On average, the algorithm showed an accuracy of 98.7 ± 0.6% in recognizing the lift task; such performance make it suitable for use in real application scenarios.
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UR - http://www.scopus.com/inward/citedby.url?scp=85055017060&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01887-0_43
DO - 10.1007/978-3-030-01887-0_43
M3 - Chapter
T3 - Biosystems and Biorobotics
SP - 224
EP - 228
BT - Biosystems and Biorobotics
PB - Springer International Publishing AG
ER -