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Video Analysis of Hand Gestures for Distinguishing Patients with Carpal Tunnel Syndrome

Video analysis of hand gestures can help distinguish patients with carpal tunnel syndrome (CTS) in three sentences: Recording and analyzing grip and release gestures of 64 participants using machine learning algorithms allowed researchers to detect CTS with 90% sensitivity and 85% specificity, outperforming conventional screening methods. The analysis involved tracking 21 feature points on participants' hands during gestures to extract motion characteristics classified by support vector machines. With further evaluation, this technique may allow simple CTS screening using smartphones.

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Video Analysis of Hand Gestures
for Distinguishing Patients
with Carpal Tunnel Syndrome
Ryota Matsui1), Takuya Ibara2), Kazuya Tsukamoto2), Takafumi Koyama2), Koji Fujita2), and Yuta Sugiura1)
1) Keio University 2) Tokyo Medical and Dental University
02 Method
01 Overview
• Compress nerves of palms
• Decline of hand motor skills
• Physical tests for CTS screening
• Elicited pain in carpal tunnel
Carpal tunnel syndrome (CTS)
05 Discussion
04 Application
03 Evaluation
• Detailed video analysis of G&R
gestures with machine learning
• Apply the analysis for patients with
CTS besides with CM
Our suggestion
• Common screening method for
cervical myelopathy (CM)
• Repeat G&R hand quickly
• Ease to perform
Grip and release (G&R) test
[Ono 1987]
Record G&R gestures
as video
Support vector
machine (SVM)
21 feature points
of hand Data processing
• Frame division to fix length
of the time-series data
• Fast Fourier transform to
extract the quickness
• CTS vs control (not patients)
• CTS vs CM
CTS
Control
/CM
Evaluation method CTS vs CM
CTS vs control
• Record G&R gestures of 64
participants with labelling
CTS/CM/control
• 10-fold cross-validation to
evaluate each SVM model
• Sensitivity = 90.0%
• Specificity = 85.3%
• AUC = 0.909
• Higher than conventional
screening methods
[Kuhlman 1997]
• Accuracy for CTS = 80.0%
• Accuracy for CM = 67.9%
• AUC = 0.787
ROC curves
ROC:
receiver operating characteristic
AUC:
area under the curve
Smartphone Cloud service
Estimate
21 points
Data
processing
SVM
Trained model
Show result
Our strength Future work
• Distinguish CTS and CM
besides the detection
• Need only smartphone
with camera
• Apply for self-tests
• Evaluate our method for
the patients with mild
symptoms
• Further association of
diseases with human
sensing techniques

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Video Analysis of Hand Gestures for Distinguishing Patients with Carpal Tunnel Syndrome

  • 1. Video Analysis of Hand Gestures for Distinguishing Patients with Carpal Tunnel Syndrome Ryota Matsui1), Takuya Ibara2), Kazuya Tsukamoto2), Takafumi Koyama2), Koji Fujita2), and Yuta Sugiura1) 1) Keio University 2) Tokyo Medical and Dental University 02 Method 01 Overview • Compress nerves of palms • Decline of hand motor skills • Physical tests for CTS screening • Elicited pain in carpal tunnel Carpal tunnel syndrome (CTS) 05 Discussion 04 Application 03 Evaluation • Detailed video analysis of G&R gestures with machine learning • Apply the analysis for patients with CTS besides with CM Our suggestion • Common screening method for cervical myelopathy (CM) • Repeat G&R hand quickly • Ease to perform Grip and release (G&R) test [Ono 1987] Record G&R gestures as video Support vector machine (SVM) 21 feature points of hand Data processing • Frame division to fix length of the time-series data • Fast Fourier transform to extract the quickness • CTS vs control (not patients) • CTS vs CM CTS Control /CM Evaluation method CTS vs CM CTS vs control • Record G&R gestures of 64 participants with labelling CTS/CM/control • 10-fold cross-validation to evaluate each SVM model • Sensitivity = 90.0% • Specificity = 85.3% • AUC = 0.909 • Higher than conventional screening methods [Kuhlman 1997] • Accuracy for CTS = 80.0% • Accuracy for CM = 67.9% • AUC = 0.787 ROC curves ROC: receiver operating characteristic AUC: area under the curve Smartphone Cloud service Estimate 21 points Data processing SVM Trained model Show result Our strength Future work • Distinguish CTS and CM besides the detection • Need only smartphone with camera • Apply for self-tests • Evaluate our method for the patients with mild symptoms • Further association of diseases with human sensing techniques