Smart Sensor-Based Motion Detection System for Hand Movement Training in Open Surgery.
We introduce a smart sensor-based motion detection technique for objective measurement and assessment of surgical dexterity among users at different experience levels. The goal is to allow trainees to evaluate their performance based on a reference model shared through communication technology, e.g....
| Publicado en: | Journal of Medical Systems Vol. 41; no. 2; pp. 1 - 14 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120895343&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120895343 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Feb2017 vid: 41 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 120895343 120895343 120895343 10.1007/s10916-016-0665-4 120895343 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Smart Sensor-Based Motion Detection System for Hand Movement Training in Open Surgery. aug: au: Sun, Xinyao Byrns, Simon Cheng, Irene Zheng, Bin Basu, Anup affil: Multimedia Research Center, Department of Computing Science , University of Alberta , Edmonton Canada sug: subj: Agility Evaluation Surgery, Computer-Assisted Evaluation Surgery, Computer-Assisted Equipment and Supplies Motion Analysis Systems Utilization Wearable Sensors Utilization Human Fingers Movement Evaluation Job Experience Comparative Studies Simulations Hand Descriptive Statistics T-Tests P-Value ab: We introduce a smart sensor-based motion detection technique for objective measurement and assessment of surgical dexterity among users at different experience levels. The goal is to allow trainees to evaluate their performance based on a reference model shared through communication technology, e.g., the Internet, without the physical presence of an evaluating surgeon. While in the current implementation we used a Leap Motion Controller to obtain motion data for analysis, our technique can be applied to motion data captured by other smart sensors, e.g., OptiTrack. To differentiate motions captured from different participants, measurement and assessment in our approach are achieved using two strategies: (1) low level descriptive statistical analysis, and (2) Hidden Markov Model (HMM) classification. Based on our surgical knot tying task experiment, we can conclude that finger motions generated from users with different surgical dexterity, e.g., expert and novice performers, display differences in path length, number of movements and task completion time. In order to validate the discriminatory ability of HMM for classifying different movement patterns, a non-surgical task was included in our analysis. Experimental results demonstrate that our approach had 100 % accuracy in discriminating between expert and novice performances. Our proposed motion analysis technique applied to open surgical procedures is a promising step towards the development of objective computer-assisted assessment and training systems. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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