Nine Hole Peg Test (NHPT) was developed to evaluate finger dexterity, also known as fine motor skill. The basic measurements include the time required to perform the test using the dominant and non-dominant hand. It can be used for a wide range of populations, including patients after stroke, multiple sclerosis, Parkinson's disease, etc., to monitor disease status and/or progression. In addition, the NHPT is relatively cheap and can be easily performed using a stopwatch. However, due to the increased workload, health professionals lack the time to regularly perform the NHPT. An autonomous test would allow frequent and repeatable monitoring of the patient's status without additional burden on the healthcare professionals.
We have developed both the hardware and software for the Autonomous Nine Hole Peg Test (ANHPT). A 3D printer was used to print a test plate with a central peg container and left-right symmetrically aligned 3$\times$3 grid of holes, thus eliminating the need to rotate the plate when switching hands. The plate was equipped with an electronic circuit that detects the presence of the peg in each of the 18 holes based on pairs of light-emitting diodes and receiving photodiodes. A rectangular stand for three cameras was also printed using a 3D printer and mounted on the plate. The ANHPT is controlled through a graphical user interface on a personal computer. This interface allows the entry of patient data, performing the NHPT according to the standard protocol, and printing out the final measurements, while in the background the software runs and communicates with the microcontroller, reads the state of the receiving photodiodes, and calibrates, captures, and analyzes video footage from the three cameras.
The measurement of the total time for inserting and removing the nine pegs for the dominant and non-dominant hand can be obtained from the electronic circuit or based on video analysis. In addition, we have obtained time measurements separately for the phases of inserting and removing the pegs. The accuracy of the ANHPT measurements was validated on healthy subjects and compared to manual measurements with a stopwatch. We found that measuring time with the electronic circuit was more accurate than capturing time with automatic video analysis. The average errors compared to manual measurements were -0.14 seconds with electronic circuit measurement and -1.01 seconds when measuring was based on recorded video analysis. However, since additional information can be extracted from the video, such as hand tremors, wrist position, and 3D hand movement trajectory, and incorrect test execution can be detected, we suggest the time to perform the test to be measured using the electronic circuit. These times can be used as auxiliary information for video analysis, thus enabling better extraction of relevant and additional information about the patient's status and to validate test execution.
During evaluation, we showed that manual measurements with a stopwatch by individual and different evaluators are affected by an average absolute error of 0.26 seconds, with a maximum error of 0.71 seconds. Given the average time for a healthy subject to perform the test, which was around 20 seconds, this is relatively a small error.
We also carried out a preliminary validation of the ANHPT in a clinical environment with healthy subjects and patients with multiple sclerosis, with the aim of confirming the ability of the ANHPT to detect differences in the time measurement the two groups. The obtained measurements matched the results of scientific articles; for instance, patients with multiple sclerosis generally took longer (average difference 7.42 seconds) to perform the test compared to healthy subjects. Differences among the patient group were larger than among healthy subject group, owing to differences in motor skills and in the variability of the stage of the disease. The latter reflected in a larger spread of time measurements, i.e. 6.69 seconds for patients with multiple sclerosis versus 1.89 seconds for healthy subjects. Analysis of the average times and standard deviation of measurements separately for the insertion and removal phases of the pegs showed that the difference between patients and healthy subjects was larger in the peg insertion phase, which is in line with observations from the scientific literature.
Our ANHPT device has proven to be accurate, reliable and robust in measuring time. In addition to the standard measurement of the total test time, it also provides additional measurements of the insertion and removal times, option to diagnose the validity of the performed test trial, plotting of the trajectory of hand movement in space and further analyses. The essential advantage of the ANHPT is the standardization of the test, as measurements are always performed according to the same protocol. In current practice with the classical test, the protocol can vary greatly from evaluator to evaluator, so results between evaluators and/or institutions are often not comparable. We assume that the ANHPT would provide more accurate and comparable measurements across institutions and medical groups and thus further establish itself as a viable tool for regular monitoring of patients' status.
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