Unobtrusive Monitoring of Physical Weakness: A Simulated Approach
- URL: http://arxiv.org/abs/2406.10045v1
- Date: Fri, 14 Jun 2024 13:52:58 GMT
- Title: Unobtrusive Monitoring of Physical Weakness: A Simulated Approach
- Authors: Chen Long-fei, Muhammad Ahmed Raza, Craig Innes, Subramanian Ramamoorthy, Robert B. Fisher,
- Abstract summary: Aging and chronic conditions affect older adults' daily lives, making early detection of developing health issues crucial.
We employ a non-intrusive camera sensor to monitor individuals' daily sitting and relaxing activities for signs of weakness.
We simulate weakness in healthy subjects by having them perform physical exercise and observing the behavioral changes in their daily activities before and after workouts.
- Score: 22.856249489748617
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Aging and chronic conditions affect older adults' daily lives, making early detection of developing health issues crucial. Weakness, common in many conditions, alters physical movements and daily activities subtly. However, detecting such changes can be challenging due to their subtle and gradual nature. To address this, we employ a non-intrusive camera sensor to monitor individuals' daily sitting and relaxing activities for signs of weakness. We simulate weakness in healthy subjects by having them perform physical exercise and observing the behavioral changes in their daily activities before and after workouts. The proposed system captures fine-grained features related to body motion, inactivity, and environmental context in real-time while prioritizing privacy. A Bayesian Network is used to model the relationships between features, activities, and health conditions. We aim to identify specific features and activities that indicate such changes and determine the most suitable time scale for observing the change. Results show 0.97 accuracy in distinguishing simulated weakness at the daily level. Fine-grained behavioral features, including non-dominant upper body motion speed and scale, and inactivity distribution, along with a 300-second window, are found most effective. However, individual-specific models are recommended as no universal set of optimal features and activities was identified across all participants.
Related papers
- Countrywide natural experiment reveals impact of built environment on physical activity [55.93314719065985]
More walkable built environments have the potential to increase activity across the population.
Increases in walkability are associated with significant increases in physical activity after relocation.
Moderate-to-vigorous physical activity (MVPA) is linked to an array of associated health benefits.
arXiv Detail & Related papers (2024-06-07T00:11:17Z) - MISO: Monitoring Inactivity of Single Older Adults at Home using RGB-D Technology [5.612499701087411]
A new application for real-time monitoring of the lack of movement in older adults' own homes is proposed.
A lightweight camera monitoring system was developed and piloted in community homes to observe the daily behavior of older adults.
arXiv Detail & Related papers (2023-11-03T21:51:33Z) - Sleep Activity Recognition and Characterization from Multi-Source
Passively Sensed Data [67.60224656603823]
Sleep Activity Recognition methods can provide indicators to assess, monitor, and characterize subjects' sleep-wake cycles and detect behavioral changes.
We propose a general method that continuously operates on passively sensed data from smartphones to characterize sleep and identify significant sleep episodes.
Thanks to their ubiquity, these devices constitute an excellent alternative data source to profile subjects' biorhythms in a continuous, objective, and non-invasive manner.
arXiv Detail & Related papers (2023-01-17T15:18:45Z) - Heterogeneous Hidden Markov Models for Sleep Activity Recognition from
Multi-Source Passively Sensed Data [67.60224656603823]
Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time.
Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles.
Mobile passively sensed data captured from smartphones constitute an excellent alternative to profile patients' biorhythm.
arXiv Detail & Related papers (2022-11-08T17:29:40Z) - Shape Analysis for Pediatric Upper Body Motor Function Assessment [1.7434874566844876]
Neuromuscular disorders, such as Spinal Muscular Atrophy (SMA) and Duchenne Muscular Dystrophy (DMD), cause progressive muscular degeneration and loss of motor function for 1 in 6,000 children.
Traditional upper limb motor function assessments do not quantitatively measure patient-performed motions.
This paper uses curve registration and shape analysis to temporally align trajectories while simultaneously extracting a mean reference shape.
arXiv Detail & Related papers (2022-09-10T17:02:31Z) - Imposing Temporal Consistency on Deep Monocular Body Shape and Pose
Estimation [67.23327074124855]
This paper presents an elegant solution for the integration of temporal constraints in the fitting process.
We derive parameters of a sequence of body models, representing shape and motion of a person, including jaw poses, facial expressions, and finger poses.
Our approach enables the derivation of realistic 3D body models from image sequences, including facial expression and articulated hands.
arXiv Detail & Related papers (2022-02-07T11:11:55Z) - RunnerDNA: Interpretable indicators and model to characterize human
activity pattern and individual difference [8.820303797376752]
The concept of RunnerDNA, consisting of five interpretable indicators, balance, stride, steering, stability, and amplitude, was proposed to describe human activity at the individual level.
We collected smartphone multi-sensor data from 33 volunteers who engaged in physical activities such as walking, running, and bicycling.
The indicators were then used to build random forest models and recognize movement activities and the identity of users.
arXiv Detail & Related papers (2022-01-19T01:09:30Z) - Estimation of Physical Activity Level and Ambient Condition Thresholds
for Respiratory Health using Smartphone Sensors [0.0]
This paper explores the potentiality of motion sensors in Smartphones to estimate physical activity thresholds that could trigger symptoms of exercise induced respiratory conditions (EiRCs)
The calculations are based on the correlation between Signal Magnitude Area (SMA) and Energy Expenditure (EE)
Real time data collected from healthy individuals were used to demonstrate the potentiality of a mobile phone as tool to regulate the level of physical activities of individuals with EiRCs.
arXiv Detail & Related papers (2021-12-11T14:25:41Z) - Designing A Clinically Applicable Deep Recurrent Model to Identify
Neuropsychiatric Symptoms in People Living with Dementia Using In-Home
Monitoring Data [52.40058724040671]
Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia.
Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions.
This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data.
arXiv Detail & Related papers (2021-10-19T11:45:01Z) - Predicting Parkinson's Disease with Multimodal Irregularly Collected
Longitudinal Smartphone Data [75.23250968928578]
Parkinsons Disease is a neurological disorder and prevalent in elderly people.
Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests.
We propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild.
arXiv Detail & Related papers (2020-09-25T01:50:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.