Video-Based Hand Movement Analysis of Parkinson Patients before and after Medication Using High-Frame-Rate Videos and MediaPipe

Tremor is one of the common symptoms of Parkinson’s disease (PD). Thanks to the recent evolution of digital technologies, monitoring of PD patients’ hand movements employing contactless methods gained momentum. Objective: We aimed to quantitatively assess hand movements in patients suffering from PD using the artificial intelligence (AI)-based hand-tracking technologies of MediaPipe. Method: High-frame-rate videos and accelerometer data were recorded from 11 PD patients, two of whom showed classical Parkinsonian-type tremor. In the OFF-state and 30 Minutes after taking their standard oral medication (ON-state), video recordings were obtained. First, we investigated the frequency and amplitude relationship between the video and accelerometer data. Then, we focused on quantifying the effect of taking standard oral treatments. Results: The data extracted from the video correlated well with the accelerometer-based measurement system. Our video-based approach identified the tremor frequency with a small error rate (mean absolute error 0.229 (±0.174) Hz) and an amplitude with a high correlation. The frequency and amplitude of the hand movement before and after medication in PD patients undergoing medication differ. PD Patients experienced a decrease in the mean value for frequency from 2.012 (±1.385) Hz to 1.526 (±1.007) Hz and in the mean value for amplitude from 8.167 (±15.687) a.u. to 4.033 (±5.671) a.u. Conclusions: Our work achieved an automatic estimation of the movement frequency, including the tremor frequency with a low error rate, and to the best of our knowledge, this is the first paper that presents automated tremor analysis before/after medication in PD, in particular using high-frame-rate video data

Analyzing the Effect of Age and Gender on the Blink Reflex using MediaPipe

The glabellar tapping reflex (GTR) is a sign related to brain conditions and can be analyzed by clinicians for diagnostic purposes. To facilitate the quantitative analysis of this reflex, we developed a video-based tool using the MediaPipe framework. We tested our approach on healthy subjects to assess the effect of age and gender on reaction time and blinking duration. The reaction time results show that the young group has a mean value (±standard deviation) of 0.091 (±0.066) seconds and the old group has 0.085 (±0.052) seconds, while female and male subjects have 0.097 (±0.053) seconds and 0.080 (±0.064) seconds respectively. For blinking duration, males have a mean value of 0.216 (±0.077) seconds and, females have 0.189 (±0.115) seconds, while old and young groups have 0.132 (±0.039) seconds 0.267(±0.084) seconds respectively