Crianza artificial de las terneras en el Módulo de Producción Lechera de la Facultad de Ciencias Agrarias

Crianza artificial de las terneras en el Módulo de Producción Lechera de la Facultad de Ciencias AgrariasFil: Galli, Julio. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias; Argentin

A Preliminary Comparison between Traditional and Gamified Leg Agility Assessment in Parkinsonian Subjects

Parkinson's disease (PD) severity is assessed through a set of standardised tasks defined by clinical scales such as the Unified Parkinson's Disease Rating Scale (UPDRS). In particular, Leg Agility is a well-established test among the motor tasks included in UPDRS, which consists in repeated cycles of knee lifting and lowering, while sitting on a chair. Leg Agility objective evaluation through optical devices is often investigated for telemedicine applications. Moreover, remote rehabilitation for PD subjects through virtual exergaming is becoming a popular approach thanks to its versatility, increased user engagement and the possibility of coupling it with remote monitoring tools. This work investigates if lower-limb exergaming may also be exploited for assessment purposes similar to traditional evaluation. In particular, if there exists a statistical difference between the kinematic description of Leg Agility versus the one of a Bouncing Ball exergame, as provided by an optical (RGB-D) acquisition system suitable for remote monitoring. Preliminary results obtained by the comparison of the two types of assessment in a small group of parkinsonian subjects are presented and discussed

Computation of Gait Parameters in Post Stroke and Parkinson’s Disease: A Comparative Study Using RGB‐D Sensors and Optoelectronic Systems

The accurate and reliable assessment of gait parameters is assuming an important role, especially in the perspective of designing new therapeutic and rehabilitation strategies for the remote follow‐up of people affected by disabling neurological diseases, including Parkinson’s disease and post‐stroke injuries, in particular considering how gait represents a fundamental motor activity for the autonomy, domestic or otherwise, and the health of neurological patients. To this end, the study presents an easy‐to‐use and non‐invasive solution, based on a single RGB‐D sensor, to estimate specific features of gait patterns on a reduced walking path compatible with the available spaces in domestic settings. Traditional spatio‐temporal parameters and features linked to dynamic instability during walking are estimated on a cohort of ten parkinsonian and eleven post‐stroke subjects using a custom‐written software that works on the result of a body‐tracking algorithm. Then, they are compared with the “gold standard” 3D instrumented gait analysis system. The statistical analysis confirms no statistical difference between the two systems. Data also indicate that the RGB‐D system is able to estimate features of gait patterns in pathological individuals and differences between them in line with other studies. Although they are preliminary, the results suggest that this solution could be clinically helpful in evolutionary disease monitoring, especially in domestic and unsupervised environments where traditional gait analysis is not usable

Assessment Tasks and Virtual Exergames for Remote Monitoring of Parkinson’s Disease: An Integrated Approach Based on Azure Kinect

Motor impairments are among the most relevant, evident, and disabling symptoms of Parkinson’s disease that adversely affect quality of life, resulting in limited autonomy, independence, and safety. Recent studies have demonstrated the benefits of physiotherapy and rehabilitation programs specifically targeted to the needs of Parkinsonian patients in supporting drug treatments and improving motor control and coordination. However, due to the expected increase in patients in the coming years, traditional rehabilitation pathways in healthcare facilities could become unsustainable. Consequently, new strategies are needed, in which technologies play a key role in enabling more frequent, comprehensive, and out-of-hospital follow-up. The paper proposes a vision-based solution using the new Azure Kinect DK sensor to implement an integrated approach for remote assessment, monitoring, and rehabilitation of Parkinsonian patients, exploiting non-invasive 3D tracking of body movements to objectively and automatically characterize both standard evaluative motor tasks and virtual exergames. An experimental test involving 20 parkinsonian subjects and 15 healthy controls was organized. Preliminary results show the system’s ability to quantify specific and statistically significant (p < 0.05) features of motor performance, easily monitor changes as the disease progresses over time, and at the same time permit the use of exergames in virtual reality both for training and as a support for motor condition assessment (for example, detecting an average reduction in arm swing asymmetry of about 14% after arm training). The main innovation relies precisely on the integration of evaluative and rehabilitative aspects, which could be used as a closed loop to design new protocols for remote management of patients tailored to their actual conditions