A pilot study for testing a low-cost 3D design with an inertial sensor for the quantitative assessment of finger tapping in patients with Parkinson's Disease.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders worldwide. Current identification and monitoring of its motor symptoms depends on the clinical expertise. Repetitive finger tapping is one of the most common clinical maneuvers to assess for bradykinesia. Despite the increasing use of technology aids to quantitatively characterize the motor symptoms of PD, there is still a relative lack of clinical evidence to support their widespread use, particularly in low-resource settings. In this pilot study, we used a low-cost design prototype coupled with an inertial sensor is coupled to quantify the frequency of the finger tapping movements in four participants with PD. Repetitive finger tapping was performed using both hands before and after taking levodopa as part of their clinical treatment. The proposed 3D design allowed repetitive movements to be performed without issues. The maximum frequency of finger tapping was in the range of 0.1 to 4.3 Hz. Levodopa was associated with variable changes in the maximum frequency of finger tapping. This pilot study shows the feasibility for low-cost technology to quantitatively characterize repetitive movements in people living with PD.Clinical relevance- In this pilot study, a low-cost inertial sensor coupled to a design prototype was feasible to characterize the frequency of repetitive finger tapping movements in four participants with PD. This method could be used to quantitatively identify and monitor bradykinesia in people living with PD.

Development of a Simulation Software Algorithm for High-End Mechanical Ventilators with Functionalities to attend COVID-19 Patients.

More than 500 millions of people were affected by the COVID-19 pandemic and in Peru there is an increasing the high numbers of cumulative cases; as well as the hospitalized people, where more than 20 % require mechanical ventilation. This condition with other respiratory diseases cause patients to remain connected to a mechanical ventilator until they regain the ability to perform this vital function on their own. Some prototypes with characteristics equivalent to a high-end mechanical ventilator have been developed. And therefore, this paper presents the design and simulation of an algorithm for the pressure-controlled pulmonary ventilation mode of the mechanical ventilator. The functional design of the algorithm uses the linear multi compartment mathematical model to simulate the respiratory system. Finally the results respond adequately under multiple scenarios, including variations of the ventilator and pulmonary parameters, where the algorithm presents encouraging results in the mechanical ventilator simulation. Clinical relevance - The algorithm presented in this study will allow to have better knowledge for a treatment and eventual clinical diagnosis in health centers, especially in eventual variants and outbreaks of COVID-19.

Combining inertial sensors and optical flow to assess finger movements: Pilot study for telehealth applications.

Parkinson's disease is the fastest growing neurological disorder worldwide. Traditionally, diagnosis and monitoring of its motor manifestations depend on examination of the speed, amplitude, and frequency of movement by trained providers. Despite the use of validated scales, clinical examination of movement is semi-quantitative, relatively subjective and it has become a major challenge during the ongoing pandemic. Using digital and technology-based tools during synchronous telehealth can overcome these barriers but it requires access to powerful computers and high-speed internet. In resource-limited settings without consistent access to trained providers, computers and internet, there is a need to develop accessible tools for telehealth application. We simulated a controlled asynchronous telehealth environment to develop and pre-test optical flow and inertial sensors (accelerometer and gyroscope) to assess sequences of 10 repetitive finger-tapping movements performed at a cued frequency of 1 Hz. In 42 sequences obtained from 7 healthy volunteers, we found positive correlations between the frequencies estimated by all modalities (ρ=0.63-0.93, P<0.01). Test-retest experiments showed median coefficients of variation of 7.04% for optical flow, 7.78% for accelerometer and 11.79% for gyroscope measures. This pilot study shows that combining optical flow and inertial sensors is a potential telehealth approach to accurately measure the frequency of repetitive finger movements.Clinical relevance- This pilot study presents a comparative analysis between inertial sensors and optical flow to characterize repetitive finger-tapping movements in healthy volunteers. These methods are feasible for the objective evaluation of bradykinesia as part of telehealth applications.