Support Vector Machine-Based Classifier for the Assessment of Finger Movement of Stroke Patients Undergoing Rehabilitation

Brunnstrom stages are not objective either so your suggested analysis falls flat on its face.  Why the fuck are you predicting classification, if you are objectively measuring this you slot them exactly into the categories you have objectively set up.

Support Vector Machine-Based Classifier for the Assessment of Finger Movement of Stroke Patients Undergoing Rehabilitation

• Authors
• Authors and affiliations

• Toyohiro Hamaguchi
• Takeshi Saito
• Makoto Suzuki
• Toshiyuki Ishioka
• Yamato Tomisawa
• Naoki Nakaya
• Masahiro Abo

• Toyohiro Hamaguchi
  • 1
  Email authorView author's OrcID profile
• Takeshi Saito
  • 1
  • 2
• Makoto Suzuki
  • 1
• Toshiyuki Ishioka
  • 1
• Yamato Tomisawa
  • 3
• Naoki Nakaya
  • 1
• Masahiro Abo
  • 4

1. 1.Department of Rehabilitation, Graduate School of Health SciencesSaitama Prefectural UniversityKoshigaya CityJapan
2. 2.Department of RehabilitationTokyo Dental College Ichikawa General HospitalChibaJapan
3. 3.Takei Scientific Instruments Corporation LimitedNiigataJapan
4. 4.Department of Rehabilitation MedicineTokyo Jikei University School of MedicineTokyoJapan

Open Access

Original Article

First Online: 12 September 2019

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Abstract

Purpose

Traditionally, clinical evaluation of motor paralysis following stroke has been of value to physicians and therapists because it allows for immediate pathophysiological assessment without the need for specialized tools. However, current clinical methods do not provide objective quantification of movement; therefore, they are of limited use(Use the correct term, useless.) to physicians and therapists when assessing responses to rehabilitation. The present study aimed to create a support vector machine (SVM)-based classifier to analyze and validate finger kinematics using the leap motion controller. Results were compared with those of 24 stroke patients assessed by therapists.

Methods

A non-linear SVM was used to classify data according to the Brunnstrom recovery stages of finger movements by focusing on peak angle and peak velocity patterns during finger flexion and extension. One thousand bootstrap data values were generated by randomly drawing a series of sample data from the actual normalized kinematics-related data. Bootstrap data values were randomly classified into training (940) and testing (60) datasets. After establishing an SVM classification model by training with the normalized kinematics-related parameters of peak angle and peak velocity, the testing dataset was assigned to predict classification of paralytic movements.

Results

High separation accuracy was obtained (mean 0.863; 95% confidence interval 0.857–0.869; p = 0.006).

Conclusion

This study highlights the ability of artificial intelligence to assist physicians and therapists evaluating hand movement recovery of stroke patients.