Fine motor assessment using automated box and block test

Abstract

The Box and Block Test (BBT) is a reliable outcome measure in upper limb rehabilitation. The present work aims to automate the test of unilateral manual dexterity of a patient's hand. A box consisting of 80 blocks was divided into two partitions. A motion tracking sensor (MPU6050) was placed on a wrist to obtain the orientation of hand movement. Thirty-four healthy participants were chosen for the test. Each participant completed both traditional and automated versions of BBT within a one-week interval, with each session lasting 60 seconds. A colour sensor at the box monitored block movements across the partitions. A Liquid Crystal Display (LCD) and a 4-digit 7-segment display provided feedback on the number of cubes moved and timing information, respectively. An independent t-test revealed a non-significant difference between traditional BBT and automated BBT. Concurrent validity was established by correlating the results of the automated BBT with the traditional BBT. A positive correlation (r= 0.94) was found between traditional and automated BBT. This validates the automated BBT report with the same result as traditional BBT. Automated BBT can assist a broad range of patients in independently tracking the progress of their hand injuries or conditions without needing a clinician. This can be conveniently done from the comfort of their homes.

Highlights

• Lack of fine motor skills and dexterity is a primary concern affecting the person’s daily living activity; hence, the automated box and block test (BBT) is valid for assessing hand dexterity quantitatively.

• The traditional BBT is an old and golden test used in dexterity assessment with neurological patients, which only provides outcomes in the form of box count, which is insufficient for hand assessment.

• The need for this work arises from the limitations of the traditional BBT, which only provides a basic outcome in the form of box counts, insufficient for comprehensive hand dexterity assessment, particularly in neurological patients.

• Automated BBT is developed, which reduces the clinician’s burden during assessment and makes the test foolproof by adding a mechanism to capture hand trajectory during block movements.

• Along with the block count, the accelerometer gives hand orientation during the test process, which helps the clinician further analyze the results, such as motion pattern analysis, temporal analysis and comparative analysis.

• The independent t-test between traditional BBT and automated BBT, p = 0.109, was observed with a 0.95 confidence interval. Levene’s test revealed no significant difference between the scores of the traditional and automated BBT (t = 2.03; p = 0.109), also between dominant and non-dominant hand (t = 1.99; p = 0.74). A positive Pearson’s correlation (r = 0.94, p < 0.05) between traditional and automated tests was obtained.