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Open AccessArticle

DataSpoon: Validation of an Instrumented Spoon for Assessment of Self-Feeding

1
Department of Physical Therapy, University of Haifa, Haifa 3498838, Israel
2
Pediatric Rehabilitation Department, Sheba Medical Center, Tel Hashomer 52621, Israel
3
Department of Occupational Therapy, University of Haifa, Haifa 3498838, Israel
4
Media Innovation Lab, The Interdisciplinary Center (IDC) Herzliya, Herzliya 4610101, Israel
5
Efi Arazi School of Computer Science, The Interdisciplinary Center (IDC) Herzliya, Herzliya 4610101, Israel
6
Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 2114; https://doi.org/10.3390/s20072114
Received: 9 February 2020 / Revised: 1 April 2020 / Accepted: 7 April 2020 / Published: 9 April 2020
(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)
Clinically feasible assessment of self-feeding is important for adults and children with motor impairments such as stroke or cerebral palsy. However, no validated assessment tool for self-feeding kinematics exists. This work presents an initial validation of an instrumented spoon (DataSpoon) developed as an evaluation tool for self-feeding kinematics. Ten young, healthy adults (three male; age 27.2 ± 6.6 years) used DataSpoon at three movement speeds (slow, comfortable, fast) and with three different grips: “natural”, power and rotated power grip. Movement kinematics were recorded concurrently using DataSpoon and a magnetic motion capture system (trakSTAR). Eating events were automatically identified for both systems and kinematic measures were extracted from yaw, pitch and roll (YPR) data as well as from acceleration and tangential velocity profiles. Two-way, mixed model Intraclass correlation coefficients (ICC) and 95% limits of agreement (LOA) were computed to determine agreement between the systems for each kinematic variable. Most variables demonstrated fair to excellent agreement. Agreement for measures of duration, pitch and roll exceeded 0.8 (excellent agreement) for >80% of speed and grip conditions, whereas lower agreement (ICC < 0.46) was measured for tangential velocity and acceleration. A bias of 0.01–0.07 s (95% LOA [−0.54, 0.53] to [−0.63, 0.48]) was calculated for measures of duration. DataSpoon enables automatic detection of self-feeding using simple, affordable movement sensors. Using movement kinematics, variables associated with self-feeding can be identified and aid clinical reasoning for adults and children with motor impairments. View Full-Text
Keywords: kinematics; concurrent validity; outcome assessment; feasibility; rehabilitation kinematics; concurrent validity; outcome assessment; feasibility; rehabilitation
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MDPI and ACS Style

Krasovsky, T.; Weiss, P.L.; Zuckerman, O.; Bar, A.; Keren-Capelovitch, T.; Friedman, J. DataSpoon: Validation of an Instrumented Spoon for Assessment of Self-Feeding. Sensors 2020, 20, 2114.

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