One-class classification with confound control for cognitive screening in older adults using gait, fingertapping, cognitive, and dual tasks.

Background And Objectives: Early detection of cognitive impairment is crucial for timely clinical interventions aimed at delaying progression to dementia. However, existing screening tools are not ideal for wide population screening. This study explores the potential of combining machine learning, specifically, one-class classification, with simpler and quicker motor-cognitive tasks to improve the early detection of cognitive impairment.

Minding your steps: a cross-sectional pilot study using foot-worn inertial sensors and dual-task gait analysis to assess the cognitive status of older adults with mobility limitations.

Background: Cognitive impairment is a critical aspect of our aging society. Yet, it receives inadequate intervention due to delayed or missed detection. Dual-task gait analysis is currently considered a solution to improve the early detection of cognitive impairment in clinical settings.

Using shoe-mounted inertial sensors and stepping exergames to assess the motor-cognitive status of older adults: A correlational study.

Objective: Stepping exergames designed to stimulate physical and cognitive skills can provide important information concerning individuals' performance. In this study, we investigated the potential of stepping and gameplay metrics to assess the motor-cognitive status of older adults.

Methods: Stepping and gameplay metrics were recorded in a longitudinal study involving 13 older adults with mobility limitations.

The feasibility of a stepping exergame prototype for older adults with major neurocognitive disorder residing in a long-term care facility: a mixed methods pilot study.

Purpose: To explore the feasibility of an exergame prototype in residential individuals with major neurocognitive disorder (MNCD).

Materials And Methods: Participants were randomly assigned to a 12-week stepping exergame training or traditional exercise (active control group). Semi-structured interviews were conducted after six and 12 weeks of exergaming.

Usability Study of a Multicomponent Exergame Training for Older Adults with Mobility Limitations.

The global population aged 60 years and over rises due to increasing life expectancy. More older adults suffer from "geriatric giants". Mobility limitations, including immobility and instability, are usually accompanied by physical and cognitive decline, and can be further associated with gait changes.

A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors.

Gait performance is an important marker of motor and cognitive decline in older adults. An instrumented gait analysis resorting to inertial sensors allows the complete evaluation of spatiotemporal gait parameters, offering an alternative to laboratory-based assessments. To estimate gait parameters, foot trajectories are typically obtained by integrating acceleration two times.

The VITAAL Stepping Exergame Prototype for Older Adults With Major Neurocognitive Disorder: A Usability Study.

This study investigates the usability of a stepping exergame in older adults with major neurocognitive disorder (MNCD) residing in a long-term care facility. A mixed methods study was conducted. Participants played exergames for 30 min on one try-out session.

Orientation-Invariant Spatio-Temporal Gait Analysis Using Foot-Worn Inertial Sensors.

Inertial sensors can potentially assist clinical decision making in gait-related disorders. Methods for objective spatio-temporal gait analysis usually assume the careful alignment of the sensors on the body, so that sensor data can be evaluated using the body coordinate system. Some studies infer sensor orientation by exploring the cyclic characteristics of walking.

Effects of an In-home Multicomponent Exergame Training on Physical Functions, Cognition, and Brain Volume of Older Adults: A Randomized Controlled Trial.

Aging is associated with a decline in physical functions, cognition and brain structure. Considering that human life is based on an inseparable physical-cognitive interplay, combined physical-cognitive training through exergames is a promising approach to counteract age-related impairments. The aim of this study was to assess the effects of an in-home multicomponent exergame training on [i] physical and cognitive functions and [ii] brain volume of older adults compared to a usual care control group.

Detection and classification of multidirectional steps for motor-cognitive training in older adults using shoe-mounted inertial sensors.

Interactive games have the potential to mitigate or prevent gait impairments and cognitive decline in older adults. This study aimed at developing a novel real-time step detection and direction classification approach to be used in the evaluation of multidirectional steps and interaction while playing motor-cognitive games. Two shoe-mounted inertial sensors were used to capture foot motions, which were treated interchangeably after the application of a novel foot sagittal reflection method.

A double closed loop to enhance the quality of life of Parkinson's Disease patients: REMPARK system.

This paper presents REMPARK system, a novel approach to deal with Parkinson's Disease (PD). REMPARK system comprises two closed loops of actuation onto PD. The first loop consists in a wearable system that, based on a belt-worn movement sensor, detects movement alterations that activate an auditory cueing system controlled by a smartphone in order to improve patient's gait.

Predicting the secondary structure of proteins using machine learning algorithms.

The functions of proteins in living organisms are related to their 3-D structure, which is known to be ultimately determined by their linear sequence of amino acids that together form these macromolecules. It is, therefore, of great importance to be able to understand and predict how the protein 3D-structure arises from a particular linear sequence of amino acids. In this paper we report the application of Machine Learning methods to predict, with high values of accuracy, the secondary structure of proteins, namely alpha-helices and beta-sheets, which are intermediate levels of the local structure.