A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults DOI Creative Commons
Loreen Pogrzeba, Evelyn Muschter, Simon Hanisch

et al.

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 29, 2025

Abstract This dataset (named CeTI-Age-Kinematics ) fills the gap in existing motion capture (MoCap) data by recording kinematics of full-body movements during daily tasks an age-comparative sample with 32 participants two groups: older adults (66–75 years) and younger (19–28 years). The were recorded using sensor suits gloves inertial measurement units (IMUs). features 30 common elemental that are grouped into nine categories, including simulated interactions imaginary objects. Kinematic under well-controlled conditions, repetitions well-documented task procedures variations. It also entails anthropometric body measurements spatial experimental setups to enhance interpretation IMU MoCap relation characteristics situational surroundings. can contribute advancing machine learning, virtual reality, medical applications enabling detailed analyses modeling naturalistic motions their variability across a wide age range. Such technologies essential for developing adaptive systems tele-diagnostics, rehabilitation, robotic planning aim serve broad populations.

Language: Английский

Digital twin systems for musculoskeletal applications: A current concepts review DOI Open Access
Pedro Diniz, Bernd Grimm, Fernando García

et al.

Knee Surgery Sports Traumatology Arthroscopy, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

Language: Английский

Citations

0
Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine DOI Creative Commons
Sakhaa B. Alsaedi, Xin Gao, Takashi Gojobori

et al.

FEBS Open Bio, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

Medical digital twins (MDTs) are virtual representations of patients that simulate the biological, physiological, and clinical processes individuals to enable personalized medicine. With increasing complexity omics data, particularly multiomics, there is a growing need for advanced computational frameworks interpret these data effectively. Foundation models (FMs), large‐scale machine learning pretrained on diverse types, have recently emerged as powerful tools improving interpretability decision‐making in precision This review discusses integration FMs into MDT systems, their role enhancing multiomics data. We examine current challenges, recent advancements, future opportunities leveraging analysis MDTs, with focus application

Language: Английский

Citations

0
New Horizons in Biomarker Discovery: Assay Technologies for Personalized Drug Development DOI
Dilpreet Singh

Assay and Drug Development Technologies, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

Language: Английский

Citations

0
Assessing the Impact of Nutritional Status on the Quality of Life in Head and Neck Cancer Patients—The Need for Comprehensive Digital Tools DOI Open Access

Rodica Maricela Anghel,

Liviu Bîlteanu,

Antonia-Ruxandra Folea

et al.

Cancers, Journal Year: 2025, Volume and Issue: 17(7), P. 1128 - 1128

Published: March 27, 2025

Background/Objectives: Malnutrition is a key determinant of quality life (QoL) in patients with head and neck cancers (HNCs), influencing treatment outcomes the occurrence adverse events (AEs). Despite there being numerous studies on nutritional status QoL, no standardized risk or prognostic model integrating clinical demographic factors. Methods: A literature search was conducted September 2024 Scopus, PubMed, Web Science, covering published between 2013 2024. Articles were selected based their relevance to AEs, interventions, QoL assessments HNC patients. Results: The factors include age, sex, weight, BMI, educational level, tumor features. Mucositis identified as most significant food intake-impairing AE, contributing malnutrition reduced QoL. Current rely descriptive questionnaires, which lack personalization predictive capabilities. Digital tools, including machine learning models digital twins, offer potential solutions for prediction personalized interventions. Conclusions: research efforts, assessment remains non-uniform, are lacking. comprehensive, approach needed, leveraging tools improve intervention strategies.

Language: Английский

Citations

0
A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults DOI Creative Commons
Loreen Pogrzeba, Evelyn Muschter, Simon Hanisch

et al.

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 29, 2025

Abstract This dataset (named CeTI-Age-Kinematics ) fills the gap in existing motion capture (MoCap) data by recording kinematics of full-body movements during daily tasks an age-comparative sample with 32 participants two groups: older adults (66–75 years) and younger (19–28 years). The were recorded using sensor suits gloves inertial measurement units (IMUs). features 30 common elemental that are grouped into nine categories, including simulated interactions imaginary objects. Kinematic under well-controlled conditions, repetitions well-documented task procedures variations. It also entails anthropometric body measurements spatial experimental setups to enhance interpretation IMU MoCap relation characteristics situational surroundings. can contribute advancing machine learning, virtual reality, medical applications enabling detailed analyses modeling naturalistic motions their variability across a wide age range. Such technologies essential for developing adaptive systems tele-diagnostics, rehabilitation, robotic planning aim serve broad populations.

Language: Английский

Citations

0