Literature DB >> 25014974

The manumeter: a wearable device for monitoring daily use of the wrist and fingers.

Nizan Friedman, Justin B Rowe, David J Reinkensmeyer, Mark Bachman.   

Abstract

Nonobtrusive options for monitoring the wrist and hand movement are needed for stroke rehabilitation and other applications. This paper describes the "manumeter," a device that logs total angular distance travelled by wrist and finger joints using a magnetic ring worn on the index finger and two triaxial magnetometers mounted in a watch-like unit. We describe an approach to estimate the wrist and finger joint angles using a radial basis function network that maps differential magnetometer readings to joint angles. We tested this approach by comparing manumeter estimates of total angular excursion with those from a passive goniometric exoskeleton worn simultaneously as seven participants completed a set of 12 manual tasks at low-, medium-, and high-intensity conditions on a first testing day, 1-2 days later, and 6-8 days later, using only the original calibration from the first testing day. Manumeter estimates scaled proportionally to the intensity of hand activity. Estimates of angular excursion made with the manumeter were 92.5% ± 28.4 (SD), 98.3% ± 23.3, and 94.7% ± 19.3 of the goniometric exoskeleton across the three testing days, respectively. Magnetic sensing of wrist and finger movement is nonobtrusive and can quantify the amount of use of the hand across days.

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Year:  2014        PMID: 25014974     DOI: 10.1109/JBHI.2014.2329841

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  20 in total

1.  Views of individuals with spinal cord injury on the use of wearable cameras to monitor upper limb function in the home and community.

Authors:  Jirapat Likitlersuang; Elizabeth R Sumitro; Pirashanth Theventhiran; Sukhvinder Kalsi-Ryan; José Zariffa
Journal:  J Spinal Cord Med       Date:  2017-07-24       Impact factor: 1.985

2.  Continuous quantitative monitoring of physical activity in Parkinson's disease patients by using wearable devices: a case-control study.

Authors:  Guoen Cai; Yujie Huang; Shan Luo; Zhirong Lin; Houde Dai; Qinyong Ye
Journal:  Neurol Sci       Date:  2017-06-28       Impact factor: 3.307

3.  A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training.

Authors:  Bruce H Dobkin
Journal:  Neurorehabil Neural Repair       Date:  2016-11-24       Impact factor: 3.919

4.  Proposal of a Wearable Multimodal Sensing-Based Serious Games Approach for Hand Movement Training After Stroke.

Authors:  Xinyu Song; Shirdi Shankara van de Ven; Shugeng Chen; Peiqi Kang; Qinghua Gao; Jie Jia; Peter B Shull
Journal:  Front Physiol       Date:  2022-06-03       Impact factor: 4.755

5.  Envisioning the use of in-situ arm movement data in stroke rehabilitation: Stroke survivors' and occupational therapists' perspectives.

Authors:  Hee-Tae Jung; Yoojung Kim; Juhyeon Lee; Sunghoon Ivan Lee; Eun Kyoung Choe
Journal:  PLoS One       Date:  2022-10-20       Impact factor: 3.752

6.  Identifying Hand Use and Hand Roles After Stroke Using Egocentric Video.

Authors:  Meng-Fen Tsai; Rosalie H Wang; Jose Zariffa
Journal:  IEEE J Transl Eng Health Med       Date:  2021-04-09       Impact factor: 3.316

Review 7.  A Systematic Review of Wearable Sensors for Monitoring Physical Activity.

Authors:  Annica Kristoffersson; Maria Lindén
Journal:  Sensors (Basel)       Date:  2022-01-12       Impact factor: 3.576

Review 8.  Wearable Sensors for Remote Health Monitoring.

Authors:  Sumit Majumder; Tapas Mondal; M Jamal Deen
Journal:  Sensors (Basel)       Date:  2017-01-12       Impact factor: 3.576

9.  An Approach to Biometric Verification Based on Human Body Communication in Wearable Devices.

Authors:  Jingzhen Li; Yuhang Liu; Zedong Nie; Wenjian Qin; Zengyao Pang; Lei Wang
Journal:  Sensors (Basel)       Date:  2017-01-10       Impact factor: 3.576

Review 10.  Computational neurorehabilitation: modeling plasticity and learning to predict recovery.

Authors:  David J Reinkensmeyer; Etienne Burdet; Maura Casadio; John W Krakauer; Gert Kwakkel; Catherine E Lang; Stephan P Swinnen; Nick S Ward; Nicolas Schweighofer
Journal:  J Neuroeng Rehabil       Date:  2016-04-30       Impact factor: 5.208

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