PRE2024 3 Group19
Group members
| Name | Student Number | Study |
|---|---|---|
| Marysia Huige | 1896431 | PT |
| Mare Hulshof | 1754440 | PT |
| Agnes Kiekebeld | 1882511 | PT |
| Lisa van den Berg | PT | |
| Stijn Schroijen | 1840150 | CS |
Subject Ideas (Brainstorm)
Subject
- Speech recognition systems.
- Make students aware of how much they use their screen (/ limit their screentime)
- Using eyetracking (?)
- Pill dispenser for elderly
- Help robots for elderly
- Music and dementia → Robot that measures when someone with dementia gets stressed (with a wristband e.g.) and puts on music from their youth when this is the case
Problem Statement
In 2020, 55 million people were diagnosed with dementia worldwide. This number is expected to grow to 139 million people in 2050. (ADI - Dementia Statistics, n.d.) Two thirds of people living in care homes have dementia. (Sharp, 2007) Dementia is a general term for a syndrome that affects memory, thinking and the ability to perform daily tasks. A common symptom of dementia is agitation. When someone is agitated, they feel irritable and restless. People are uncooperative and have difficulties focusing when feeling agitated. They can also show violent or disruptive behavior and have a lack of impulse control. This negative and dangerous behavior could cause dangerous and unpleasant situations for both people with dementia but also for their family, friends, and staff of care homes.
This project aims to create an aid that uses music to help calm down patients with dementia when they are feeling agitated.
Objectives
The main objective of this research is to find a way to calm dementia patients down when they are feeling stressed. It is quite common for people with dementia to be distressed, which can be caused by feeling disoriented, having unmet needs, as well as many other factors (Coping with distress - Dementia UK). It has been found that music interventions lower anxiety levels and generally make patients (and even caregivers) feel more relaxed (Impacts of Music Intervention on Dementia: A Review Using Meta-Narrative Method and Agenda for Future Research). That is why music will be implemented in the design not alleviate stress In order to further assess what we want our design to achieve we will be using the MoSCoW method.
Must have
- A calming effect on the patient
- A stress sensor of some kind to determine when the music should be turned on
Should have
- Music from the patients youth
- Comfortability (if the stress sensor is wearable)
Could have
- A calming effect on the caregiver
- Slowly increase volume to limit the shock
- A wearable stress sensor, e.g. using HRV in the same way that a watch measures stress [1]
- A stress sensor based on sound
Will not have
- Scaring the patient by sudden loud music
Users (and their requirements)
The target users of this design are people with dementia, particularly those in the mid-to-late stages of the condition. Dementia affects cognitive functions such as reasoning, memory, and emotional regulation, making patients more prone to stress, anxiety, and agitation. This has detrimental effects on their well-being.
Given the cognitive impairments associated with dementia, the design of the aid to measure stress and play music must function autonomously with minimal user input. Furthermore, as most individuals with dementia belong to an older demographic, potential physical limitations should be taken into account. It is essential that the stress sensor is easy to use and imposes minimal physical constraints to ensure comfort and practicality. Potential perceptual limitations are also important to consider. The device for playing music must be able to adjust its volume settings to an appropriate level. The music should be loud enough for the patient to actually perceive the music, but it should not be too loud, since this could only contribute to the distress.
Another crucial user requirement is the personalization of the aid. Both in the measurement of stress and for the intervention in the form of playing music personalization is needed. Personalization allows the aid to calibrate to a user's unique stress patterns, ensuring more accurate responses. Furthermore, since music preferences vary from person to person, the system must be capable of playing the specific music that elicits positive emotions for each individual. This personalization is essential for effectively reducing stress levels and providing a soothing experience.
Since the caregivers of people with dementia are often also affected by distress of these individuals, they can be considered secondary users. They play a vital role in setting up and managing the device, selecting appropriate music and monitoring its effectiveness. To avoid adding to their workload, the system must be as simple to use as possible.
Stress Measurement Techniques for Wearable Devices
Stress is a physiological and psychological response to external stimuli, influencing various bodily functions such as skin conductance, heart rate and brain activity. Wearable sensors that for monitoring stress are increasingly incorporated in consumer products such as smart watches. However, selecting the right combination of sensors for a wristband involves balancing accuracy, comfort, and practicality. In this section we explore several different stress measurement techniques and evaluate which are the best options to implement in wrist-worn wearables.
Electrocardiography (ECG) - Heart Activity Measurement
ECG measures the electrical activity of the heart, specifically heart rate variability (HRV). A lower HVR often indicates higher stress levels. In order to be able to conduct an ECG, electrodes are placed on the skin, commonly in the form of a chest strap or adhesive patches.[1]
Advantages
- It offers a highly accurate form of stress detection
- It directly measures heart rate fluctuations related to autonomic nervous system activity.
Disadvantages
- It requires precise placement, which is difficult when used in daily life
- It produces large amounts of data, requiring professional interpretation
- It is not easily integrated into a wristband due to placement limitations
Photoplethysmography (PPG) - Optical Heart Rate Monitoring
PPG uses LED light reflection to measure blood volume changes in the capillaries, indirectly determining heart rate and HRV. An LED-light is shown onto the skin and a sensor measures the amount of light that is reflected back. The more light is reflected back, the lower the blood volume. This technology is widely integrated into smartwatches and wristbands.[1]
Advantages:
- The sensor is small, requires little power and is suitable for wearables.
- Provides real-time heart rate and HRV data
Disadvantages:
- less accurate than ECG, especially in high-motion environments
- Affected by skin tone, environmental light, and sensor placement
Galvanic Skin Response (GSR) - Skin Conductance Measurement
GSR measures skin conductivity, which increases as sweat glands activate under stress. This is a direct indicator of autonomic nervous system activity. GSR works through electrodes on the skin that measure the electrical conductivity of the skin.[1]
Advantages:
- Quick detection of emotional responses
- Works well for short-term stress measurement
Disadvantages:
- It is sensitive to environmental factors like humidity and temperature
- contact with the skin is needed and proper positioning for accurate results.
Skin Temperature (ST)
Stress can cause fluctuations in peripheral skin temperature due to changes in blood flow. Wrist-worn thermistors or infrared sensors can detect these changes.[2]
Advantages:
- Non-invasive and easy to integrate.
Disadvantages:
- It is not always a direct stress indicator. Temperature can fluctuate due to other factors. It is more suitable as a secondary sensor that can complement more accurate sensors.
Blood Pressure (BP) and Blood Oxygen Saturation (SpO2)
Blood pressure increases under stress, however it can also fluctuate due to other physiological factors.
Stress and anxiety can alter an individual's breathing, thereby altering the oxygen saturation in the blood. [2]
Advantages:
- Provides additional physiological insights
- Blood Pressure is linked to cardiovascular health, making it useful beyond stress monitoring.
Disavantages:
- Wrist-based blood pressure measurements lack clinical accuracy
- Oxygen saturation fluctuations are not always directly linked to stress.
Which indicators and sensors to use
Based on the strengths and weaknesses of each technology, the best combination of sensors to incorporate in a wristband would include either measures of heart rate variability (preferably PPG) or sensors of Galvanic skin response (GSR) as primary sensors. As an addition, skin temperature sensors or blood pressure sensors can be implemented as secondary sensors to enhance accuracy.
Approach, milestones and deliverables
Approach
Overall plan, steps, etc.
Start by doing a literature study. We want to make/ build a prototype and then interview healthcare workers on their opinions on our idea.
| Week 1 | Week 2 | Week 3 | Week 4 | Week 5 | Week 6 | Week 7 |
|---|---|---|---|---|---|---|
| 13/02 Mail with topic and group | Start looking for interviewees | Start on prototype* | Work on prototype | Finalise and test the prototype (make adjustments if necessary) | Prepare presentation | Presentation |
| Set up project | Work on the design of the prototype | Set up the interview schedule | Make informed consent form | Interview healthcare workers on prototype* | Interpret results from interviews | |
| Search and summarize 25 research papers | Order all necessary components | Make sketch for prototype | ||||
| Work on Problem statement, Objectives, Users, approach, Milestones, Deliverables,
and Planning |
*rough estimate in planning
Milestones
What are the big checkpoints, so how long will we be working on each section e.g.
Week 1:
- Finish Problem statement, Objectives, Users, approach, Milestones, Deliverables, and Planning sections
- Finish State of the art section
Week 2:
- Finish making sketches
- Finalise the prototype design
Week 4:
- Finalise Informed consent form
- Finalise interview schedule
Week 5:
- Finalise the prototype and receive feedback
Week 6:
- Receive all data from the interviews
Week 7:
- Give final presentation
Deliverables
What we will be handing in (so are we making a prototype, literature study, etc.)
- Fuctional prototype
- Result of the interviews
- Report about the project in this Wiki
Planning
Taakverdeling (is this needed when we have a log book?)
State of the art
Literature study with at least 25 relevant papers
Marysia:
Effects of Music on Agitation in Dementia: A Meta-Analysis
https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2017.00742/full
Pedersen et al. performed a meta-analysis to (1) investigate whether music therapy has a positive effect on reducing agitation for patients with dementia. Furthermore, they (2) compare personalized and group music intervention. Besides that, Pedersen (3) evaluates the effect of preferences in music by comparing music based on patient preference without prior consultation with patients or caregivers. Lastly, the study (4) focusses on comparing the benefits of active versus passive music intervention. In active music intervention, participants actively engage by singing, dancing, or playing an instrument. In passive music intervention, participants listen to live or recorded music without actively engaging.
The results of this meta-analysis indicate that (1) music intervention does significantly decrease agitation in patients with dementia. Furthermore, the analysis (2) shows that personalized music therapy may have a larger effect in certain cases, however the effect is less consistent across the different studies used in this research. Both individual and prescribed music preferences (3) show a medium effect size. Finally, both active and passive music interventions have similar effects. Passive interventions may work better in different settings.
The study performed by Pedersen et al. indicated that music intervention has a reducing effect on agitation of patients with dementia.
The Impact of Music on the Self in Dementia
https://www.researchgate.net/publication/322358822_The_Impact_of_Music_on_the_Self_in_Dementia
This study proposes a theoretical framework which enhances the sense self in patients with dementia. To explain this framework, numerous features of music have been mentioned: Engaging, Persuasive, Social, Synchronization, Physical, Emotional, and Personal.
This framework explains how music engages the five aspects of one’s self, namely ecological, interpersonal, private, extended, and conceptual self.
The ecological self is the self has it is perceived through sensory information and the environment. Numerous features of music, such as physical, synchronous, engaging, and emotional, are important for this self. The interpersonal self can be explained as the self that socially interacts with others. Here the persuasive and engaging aspects of music play a key role. The extended self shows how the self was in the past and how it might be in the future. The different way of engaging with music, and the personal and emotional features of music contributed to engaging the extended self for people with dementia. The private self exists of experiences that others do not have access to, such as thoughts, beliefs, and experiences. Music can help this self as a new way to express themselves. Finally, the conceptual self is indicated the concept that the individual has of themselves, for example self-identity, roles, and social identity.
The researchers of this paper identify the music features and the aspects of the self using two case studies and conclude that music has a unique ability to engage with multiple aspects of the self.
Music and dementia: Observing effects and searching for underlying theories
https://pubmed.ncbi.nlm.nih.gov/21069595/
This research paper studies whether the effect of music on dementia is beneficial and if so, why that is the case. The article focusses on three key areas, namely “music and memory”, “music and depression”, and “Agitation and aggressive behaviors”.
The article states that some people with dementia remember music longer than they remember and respond to other types of information.
Reminiscence music therapy, music therapy that uses familiar songs to trigger memories of the individual with dementia, has reduced working on symptoms of depression. This could be explained by the fact that music induced physical and emotional changes. Furthermore, music could help reduce stress, sooth pain and energize the body. Therefore, music could be used as an aid for mood regulations.
Moreover, music has a beneficial influence on reducing agitated and aggressive behaviors of individuals with dementia during various activities and various times. Playing music that is preferred by the participant has the biggest outcome. Music makes sounds more familiar and predictable. This could lead to creating a positive emotional state as people retrieve positive memories.
Music and the wellbeing of people with dementia
https://www.researchgate.net/publication/231844344_Music_and_the_wellbeing_of_people_with_dementia
In this study, Sixsmith and Gibson perform qualitative research on the influence that music has on the everyday life of participants with dementia. In-depth interviews were conducted with a sample size consisting of 26 patients with dementia living in either their own homes or in residential care. This paper highlights the benefits of music but also the problems that could arise while listening or actively participating in musical activities.
The data from the interviews shows that the beneficial effect of having music in the lives of people with dementia could enhance the feelings of wellbeing. Music could make people with dementia ready to express their happiness both physically and emotionally.
Furthermore, music supports valued activities. Playing music in the background while working on a task could make the task more enjoyable for people with dementia.
Moreover, music encourages social interaction. During musical activities, most of the participants were accompanied by their family, carers or by other people with dementia. Music therefore is a way for people with dementia to communicate with other sand have meaningful social interactions.
Lastly, music gives people with dementia a higher degree of empowerment and control over their own lives.
Sixsmith and Gibson state multiple challenges that arise when people with dementia engage with music, such as difficulties expressing their preferences, hearing difficulties, the loss of confidence, and social barriers in care homes.
The importance of music for people with dementia: the perspectives of people with dementia, family carers, staff and music therapists
https://pubmed.ncbi.nlm.nih.gov/24410398/
This research paper focused on the meaning and value of musical intervention for not only people with dementia, but also their families, care home staff and music therapists. McDermott et al. are aiming to explore the meaning and value of music from the people with dementia point of view. The paper also analyses how families, care home staff and music therapists view the effects of music. Furthermore, the paper aims to find a link between psychosocial factors and the results of the study to be able to develop a theoretical musical framework of music in dementia.
The qualitative data was ‘gotten’ from conducting focus groups and interviews with care home residents with dementia and their families, care home staff, music therapies, and day hospital clients with dementia.
From the data received, it can be concluded that meaningful musical experience could result in people with dementia feeling emotionally connected with other people. Moreover, musical activities lead to social interaction between residents and staff in care homes. That has resulted in a more positive impact on the environment of the care home.
The researchers also state that music is linked to personal identity and life history of people with dementia.
Agnes
More thorough summaries after meeting.
Impacts of Music Intervention on Dementia: A Review Using Meta-Narrative Method and Agenda for Future Research [2]
This paper shows a meta analysis of the effect of music on dementia patients. Several positive effects of music on patients with dementia have been established in different fields, namely biologically (hr, etc.), behaviorally (less stress, agression, etc.), cognitively (better speech e.g.) and emotionally (more enjoyment in life). It is however mainly focused on guided music intervention, where the patient is encouraged to sing along, etc.
Integrated mental stress smartwatch based on sweat cortisol and HRV sensors [3]
A measurement technique that appears to work quite nicely to measure stress based on sweat cortisol and HRV.
How and why music therapy reduces distress and improves well-being in advanced dementia care: a realist review [4]
Once again mainly focused on music therapy with a music therapist , however that appears to have great effects on patients!
Wearables for residents of nursing homes with dementia and challenging behaviour: Values, attitudes, and needs [5]
What are the requirements for a wearable for patients with dementia?
- Not too tight around the wrist, as that hurts
- Should be recognizable for patients with dementia (as a watch e.g.)
- The patient should not be able to turn the device off themselves by a button on the outside (as some patients turned it off themselves)
- Water resistance
- Small sizes (not too bulky)
- Customizable
Perceived usefulness and comfortability are the most important. There are currently no wearables that can measure stress "live" and are comfortable so there is room for improvement!
User Requirements and Perceptions of a Sensor System for Early Stress Detection in People With Dementia and People With Intellectual Disability: Qualitative Study [6]
Once again what does a stress sensor system for patients with dementia need?
- Integrating it into clothing/ wristbands for familiarity purposes
- Comfortable fabric/ fit
- Customizability to increase user acceptance
- Visibility - Not that visible to others and as discreet as possible
- Easy to put on
Most important: washable and safe!
Perceived positives: identifying stress and using early intervention methods
Perceived negatives: the system not being accepted by the users, replacing the human aspect of care
Lisa
Effects of Music Therapy on Patients with Dementia—A Systematic Review
https://www.mdpi.com/2308-3417/5/4/62
Music and Other Strategies to Improve the Care of Agitated Patients with Dementia
Individualized music played for agitated patients with dementia: Analysis of video-recorded sessions
Continuous stress detection using the sensors of commercial smartwatch
Stress Watch: The Use of Heart Rate and Heart Rate Variability to Detect Stress: A Pilot Study Using Smart Watch Wearables
https://www.mdpi.com/1424-8220/22/1/151
Mare
Technology and Dementia: The Future is Now | Dementia and Geriatric Cognitive Disorders | Karger Publishers
https://karger.com/dem/article/47/3/131/103431
This article summarizes several key areas of technological development in dementia care. The main domains in which there is a lot of technological advancement include: 1) diagnosis assessment and monitoring, 2) maintenance of functioning, 3) leisure and activity, and 4) caregiving and management.
Examples of technological advances in the domain of leisure and activity also include systems that use music to soothe patients. In the paper a one-button radio is mentioned, but also a simple interface for making music and systems for collaborative music making.
The article ends with the conclusion that, despite numerous technological initiatives in dementia care, adoption rates remain low. Contributing factors include a lack of awareness, challenges in accessibility and insufficient support. The paper emphasizes the need for policies, funding, and practices that go beyond a purely medical approach, advocating for a holistic perspective that includes prevention strategies and ways to support a meaningful life with dementia.
Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia
https://www.mdpi.com/1424-8220/16/12/1989
This paper describes a study in which the effectiveness of measuring the stress levels in people with dementia using a single wristband sensor is explored. Stress is a common issue for individuals with dementia, and it is traditionally assessed through questionnaires completed by a caregiver. This is a time-consuming and subjective process. In this research, a wearable sensor that tracks galvanic skin response (GSR) and movement is assessed in its effectiveness of detecting stress episodes.
During the study, the sensor continuously recorded physiological data, which was processed using an online platform to classify the stress levels on a scale from 0 to 5. The goal was to find out if the identified stress episodes by the sensor correlated with those observed by the caregiver.
The results showed that there was a strong correlation between the data from the sensor and the caregiver observations. There were, however, strong variations in stress levels among individuals, which highlights the need for personalized stress thresholds. This can improve accuracy in detection.
The conclusion of the researchers is that a wristband sensor is a promising tool for monitoring stress. It provides objective, real-time data, which can help caregivers in for example recognizing stress patterns and intervening more effectively.
Stress as Experienced by People with Dementia: An Interpretative Phenomenological Analysis - Barbara K Sharp, 2019
In this paper, a study is described on the subject of stress as experienced by people with dementia. The study contributes to the supporting of people with dementia, by creating a greater understanding about how people with dementia make meaning from their experience of stress. The questions of interest where how people with dementia perceive their experiences of stress and how they cope with these.
It was found that individual personality traits and established coping styles persist and sustain influence in managing stress. This is contrary to previous assumptions that dementia is paired with an inevitable decline and progressive vulnerability to stress. It was furthermore demonstrated that people with Alzheimer's disease demonstrate a preference for "emotion focused" over "problem solving" approaches.
A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques | IEEE Journals & Magazine | IEEE Xplore
https://ieeexplore.ieee.org/abstract/document/9445082
This paper provides a review of several methods for detecting mental stress in different environments using wearable sensors and machine learning techniques.
Stress is defined as a psycho-physiological response to challenges that can lead to severe health issues. Wearable sensors collect biological signals that reflect the body's response to stressors. Advantages of wearable sensors include real-time monitoring and non-intrusiveness.
In the article, various physiological signals which have potential to be a measure of stress are considered. Each signal provides unique insights into how the body responds to stress. Some wearables integrate multiple of these sensors to measure stress more effectively.
The article also discusses some challenges and limitations of wearable sensor-based stress detection.
Remote Healthcare for Elderly People Using Wearables: A Review
https://www.mdpi.com/2079-6374/12/2/73
This article discusses the use of wearable sensors for the remote monitoring of elderly individuals. There is a specific focus on how wearables can contribute to healthcare. Several physiological variables that can be measured are discussed, among which heart rate (variability), blood pressure and oxygen saturation.
The article makes a distinction between commercial wearables such as smart watches and research prototypes such as smart textiles. It is stated that the commercial wearables are increasing in their reliability of sensors.
Several benefits and challenges of wearables in elderly care are discussed. Some of the challenges include the limited battery life of wearables and user acceptance. It is however concluded that wearables provide a promising solution for elderly healthcare by enabling continuous monitoring and early disease detection.
Stijn
The promise of music therapy for Alzheimer's disease: A review
https://pmc.ncbi.nlm.nih.gov/articles/PMC9796133/
The article explores the potential benefits of music therapy (MT) in managing Alzheimer's disease (AD). Despite significant impairments in episodic and semantic memory, patients with AD often retain musical memory. This preservation enables them to learn new songs, encode novel verbal information, and respond emotionally to music. These observations have led to the development of MT as a nonpharmacological intervention for AD.
Studies indicate that MT can improve mood, reduce depressive symptoms and anxiety, enhance autobiographical recall, boost verbal fluency, and support cognitive functions in AD patients. Given its ease of implementation and high tolerance among patients and caregivers, MT is a promising therapeutic approach. The authors suggest that early music interventions might delay or slow neurodegeneration in individuals at risk for AD, such as those with genetic predispositions or subjective cognitive decline.
The Effects of Music Therapy on Cognition, Psychiatric Symptoms, and Activities of Daily Living in Patients with Alzheimer’s Disease
https://content.iospress.com/articles/journal-of-alzheimers-disease/jad180183
The effect of music therapy on cognitive functions in patients with dementia: a systematic review and meta-analysis
https://www.tandfonline.com/doi/full/10.1080/13607863.2017.1348474#abstract
Dementia and the Power of Music Therapy
https://onlinelibrary.wiley.com/doi/full/10.1111/bioe.12148
Influence of Music Therapy and Music-Based Interventions on Dementia: A Pilot Study
https://academic.oup.com/jmt/article/58/3/e12/6265007?login=true
Log Book
Add the hours of work you spent in a week, what tasks you worked on and how long those subtasks took inside of your own table. Per week we should be able to see the total and there will also be a grand total at the end of the project.
| Week | Task | Hours |
|---|---|---|
| 1 | Brainstorm | 1 |
| Search literature studies | 2 | |
| Summarize literature studies | 5 | |
| Problem statement | 1 | |
| Approach, Milestones, and Deliverables | 2 | |
| Total | 11 | |
| 2 | Meeting monday | 1 |
| Research about stress sensors | 3 | |
| Summarizing findings | 3 | |
| Meeting friday | 1 | |
| Total | 8 |
| Week | Task | Hours |
|---|---|---|
| 1 | Brainstorm | 1 |
| Search literature studies | 1.5 | |
| Summarize literature studies | 5.5 | |
| Users (and their requirements) | 2 | |
| Total | 10 | |
| 2 |
| Week | Task | Hours |
|---|---|---|
| 1 | Brainstorm | 1 |
| Setting up the wiki page | 2 | |
| Search literature studies | 2 | |
| Summarize literature studies | 3 | |
| Objectives | 2 | |
| Total | 10 | |
| 2 | Meeting monday | 1 |
| Prototype meeting thursday | 1 | |
| Prototype research | 4 | |
| Meeting friday | 1 | |
| Brainstorm after meeting | 1 | |
| Finish summarizing (not yet finished) | ||
| Total | 8 |
| Week | Task | Hours |
|---|---|---|
| 1 | Brainstorm | 1 |
| Search literature | 1 | |
| Total | 2 | |
| 2 |
| Week | Task | Hours |
|---|---|---|
| 1 | Brainstorm | 1 |
| Search literature | 1.5 | |
| Total | 2.5 | |
| 2 |
References
- ↑ 1.0 1.1 1.2 S. Gedam and S. Paul, "A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques," in IEEE Access, vol. 9, pp. 84045-84066, 2021, doi: 10.1109/ACCESS.2021.3085502. keywords: {Stress;Heart rate variability;Resonant frequency;Wearable sensors;Heart rate;Skin;Frequency estimation;Mental stress detection;machine learning;physiological signals;wearable sensor;feature extraction},
- ↑ 2.0 2.1 G. Taskasaplidis, D. A. Fotiadis and P. D. Bamidis, "Review of Stress Detection Methods Using Wearable Sensors," in IEEE Access, vol. 12, pp. 38219-38246, 2024, doi: 10.1109/ACCESS.2024.3373010. keywords: {Human factors;Temperature measurement;Stress measurement;Stress;Heart rate;Anxiety disorders;Wearable devices;Detection algorithms;Wearable sensors;Stress analysis;stress detection;stress response;wearable sensors},
- ADI - Dementia Statistics. (n.d.). ADI - Dementia Statistics. https://www.alzint.org/about/dementia-facts-figures/dementia-statistics/
- 2. Sharp, S. (2007). Home From Home : A report highlighting opportunities for improving standards of dementia care in care homes. Alzheimer’s Society. https://europepmc.org/abstract/CTX/c3064