Personal Data Protection in Smart Home Activity Monitoring for Digital Health: A Case Study
- URL: http://arxiv.org/abs/2504.13864v1
- Date: Thu, 27 Mar 2025 16:35:55 GMT
- Title: Personal Data Protection in Smart Home Activity Monitoring for Digital Health: A Case Study
- Authors: Claudio Bettini, Azin Moradbeikie, Gabriele Civitarese,
- Abstract summary: Sensor-based human activity recognition (HAR) enables identification of behavioral changes that clinicians consider as a digital bio-marker of early stages of cognitive decline.<n>The real deployment of sensor-based HAR systems in the homes of elderly subjects poses several challenges, with privacy and ethical concerns being major ones.<n>This paper reports our experience applying privacy by design principles to develop and deploy one of these systems.
- Score: 0.3277163122167433
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Researchers in pervasive computing have worked for decades on sensor-based human activity recognition (HAR). Among the digital health applications, the recognition of activities of daily living (ADL) in smart home environments enables the identification of behavioral changes that clinicians consider as a digital bio-marker of early stages of cognitive decline. The real deployment of sensor-based HAR systems in the homes of elderly subjects poses several challenges, with privacy and ethical concerns being major ones. This paper reports our experience applying privacy by design principles to develop and deploy one of these systems.
Related papers
- Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
Mental illness is a widespread and debilitating condition with substantial societal and personal costs.<n>Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder.<n>Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
arXiv Detail & Related papers (2025-02-01T15:10:02Z) - Modeling User Preferences via Brain-Computer Interfacing [54.3727087164445]
We use Brain-Computer Interfacing technology to infer users' preferences, their attentional correlates towards visual content, and their associations with affective experience.
We link these to relevant applications, such as information retrieval, personalized steering of generative models, and crowdsourcing population estimates of affective experiences.
arXiv Detail & Related papers (2024-05-15T20:41:46Z) - Real-Time Elderly Monitoring for Senior Safety by Lightweight Human
Action Recognition [11.178325140443446]
Real-time monitoring and action recognition essential to raise an alert timely when abnormal behaviors or unusual activities occur.
We propose a novel Real-time Elderly Monitoring for senior Safety (REMS) based on lightweight human action recognition (HAR) technology.
arXiv Detail & Related papers (2022-07-21T15:00:54Z) - Classifying Human Activities with Inertial Sensors: A Machine Learning
Approach [0.0]
Human Activity Recognition (HAR) is an ongoing research topic.
It has applications in medical support, sports, fitness, social networking, human-computer interfaces, senior care, entertainment, surveillance, and the list goes on.
We examined and analyzed different Machine Learning and Deep Learning approaches for Human Activity Recognition using inertial sensor data of smartphones.
arXiv Detail & Related papers (2021-11-09T08:17:33Z) - 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) - Learning Language and Multimodal Privacy-Preserving Markers of Mood from
Mobile Data [74.60507696087966]
Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care.
One promising data source to help monitor human behavior is daily smartphone usage.
We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
arXiv Detail & Related papers (2021-06-24T17:46:03Z) - Anxiety Detection Leveraging Mobile Passive Sensing [53.11661460916551]
Anxiety disorders are the most common class of psychiatric problems affecting both children and adults.
Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods.
eWellness is an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual's device in a continuous and passive manner.
arXiv Detail & Related papers (2020-08-09T20:22:52Z) - Human Activity Recognition using Inertial, Physiological and
Environmental Sensors: a Comprehensive Survey [3.1166345853612296]
This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.
Har is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities.
arXiv Detail & Related papers (2020-04-19T11:32:35Z) - AutoCogniSys: IoT Assisted Context-Aware Automatic Cognitive Health
Assessment [2.7998963147546148]
AutoCogniSys is a context-aware automated cognitive health assessment system.
We develop an automatic cognitive health assessment system in a natural older adults living environment.
The performance of AutoCogniSys attests max. 93% of accuracy in assessing cognitive health of older adults.
arXiv Detail & Related papers (2020-03-17T01:44:59Z) - Online Guest Detection in a Smart Home using Pervasive Sensors and
Probabilistic Reasoning [3.538944147459101]
This paper presents a probabilistic approach able to estimate the number of persons in the environment at each time step.
Using both simulated and real data, our method has been tested and validated on two smart homes of different sizes and configuration.
arXiv Detail & Related papers (2020-03-13T15:41:15Z) - EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies
on Signal Sensing Technologies and Computational Intelligence Approaches and
their Applications [65.32004302942218]
Brain-Computer Interface (BCI) is a powerful communication tool between users and systems.
Recent technological advances have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications.
arXiv Detail & Related papers (2020-01-28T10:36:26Z)
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.