BACKGROUND There is a global challenge related to the increase in the number of people with dementia (PwD) and the diminishing capacity of governments, health systems, and caregivers to provide the best care for them. Cost-effective technology solutions that enable and ensure a good quality of life for PwD via monitoring patients and interventions have been investigated comprehensively in the literature. OBJECTIVE The objective of this study was to investigate the challenges with the design, deployment and functioning of a Smart Home In a Box (SHIB) approach to monitoring PwD wellbeing within a care home. This could then support future implementations and present further opportunities for the SHIB approach. An important consideration was that most care homes do not have the appropriate infrastructure for installing and using ambient sensors. METHODS The SHIB was evaluated via installation in the rooms of 3 PwD with varying degrees of dementia. Sensors from the SHIB were installed in the rooms of the PwD to test the capabilities of these sensors for detecting Activities of Daily Living (ADLs). The sensors used were (i) thermal sensors, (ii) contact sensors, (iii) Passive Infrared (PIR) sensors, and (iv) audio level sensors. Data was collected, stored and handled using a ‘SensorCentral’ data platform. RESULTS This study highlighted challenges and opportunities that should be considered when installing and using a SHIB approach in a dementia care home. Lessons learned from this investigation are presented in addition to recommendations that could support the wellbeing monitoring of PwD. This study also presents results from initial data analysis and demonstrates how activities, falls and abnormal behaviors could be detected and acted upon. CONCLUSIONS This study was conducted by Ulster University’s Pervasive Computing Research Group (PCRG) as part of the Northern Ireland Connected Health Innovation Centre (NI-CHIC) project. The design, deployment and functioning of the SHIB approach within Kirk House Care Home in Belfast provided the community with useful lessons, that will continue to be applied to improve future implementations of the SHIB approach. Several challenges arose during the installation, functioning and data collection at Kirk House Care Home, for example, regarding the adaption of technology to a building that was not originally designed for the integration of ambient sensors. The main findings of this study are (i) most care home buildings were not originally designed to appropriately install ambient sensors, and (ii) installation of SHIB sensors should be adapted depending on the specific case of the care home where they will be installed. It was acknowledged that in addition to care homes, the homes of dementia patients were also not designed for an appropriate integration with ambient sensors. Hence, another possible use of a SHIB approach.
Activity recognition is one of the most active areas of research in ubiquitous computing. In particular, gait activity recognition is useful to identify various risk factors in people's health that are directly related to their physical activity. One of the issues in activity recognition, and gait in particular, is that often datasets are unbalanced (i.e., the distribution of classes is not uniform), and due to this disparity, the models tend to categorize into the class with more instances. In the present study, two methods for classifying gait activities using accelerometer and gyroscope data from a large-scale public dataset were evaluated and compared. The gait activities in this dataset are (i) going down an incline, (ii) going up an incline, (iii) walking on level ground, (iv) going down stairs, and (v) going up stairs. The proposed methods are based on conventional (shallow) and deep learning techniques. In addition, data were evaluated from three data treatments original unbalanced data, sampled data, and augmented data. The latter was based on the generation of synthetic data according to segmented gait data. The best results were obtained with classifiers built with augmented data, with F-measure results of 0.812 (s = 0.078) for the shallow learning approach, and of 0.927 (s = 0.033) for the deep learning approach. %Therefore, it can be concluded that deep-approach is better than shallow-approach for all data treatments. In addition, the data augmentation strategy proposed to deal with the unbalanced problem resulted in increased classification performance using both techniques.
Recently, the number of People with Dementia (PwD) has been rising exponentially across the world. The main symptoms that PwD experience include impairments of reasoning, memory, and thought. Owing to the burden faced by this chronic condition, Assistive Technology-based solutions (ATS) have been prescribed as a form of treatment. Nevertheless, it is widely acknowledged that low adoption rates of ATS have hampered their benefits within a health and social care context. It is then necessary to effectively discriminate between adopters and non-adopters of such solutions to avoid cost implications, improve the life quality of adopters, and find intervention alternatives for non-adopters. Several classifiers have been proposed as advancement towards the personalisation of self-management interventions for dementia in a scalable way. As multiple algorithms have been developed, an important step in technology adoption is to select the most appropriate classification alternative based on different criteria. This paper presents the integration of Fuzzy AHP (FAHP) and VIKOR to address this challenge. First, FAHP was used to calculate the criteria and sub-criteria weights under uncertainty and then VIKOR was implemented to rank the classifiers. A case study considering a mobile-based self-management and reminding solution for PwD is described to validate the proposed approach. The results revealed that Easiness of interpretation (GW = 0.192) and Handling of missing data (GW = 0.145) were the two most important criteria. Furthermore, SVM (Qj = 1.0) and AB (Qj = 0.891) were concluded to be the most suitable classifiers for supporting ATS adoption in PwD.
Introduction As high amounts of occupational sitting have been associated with negative health consequences, designing workplace interventions to reduce sedentary behaviour (SB) is of public health interest. Digital technology may serve as a cost-effective and scalable platform to deliver such an intervention. This study describes the iterative development of a theory-based, digital behaviour change intervention to reduce occupational SB. Methods The behaviour change wheel and The Behaviour Change Technique Taxonomy were used to guide the intervention design process and form a basis for selecting the intervention components. The development process consisted of four phases phase 1 – preliminary research, phase 2 – consensus workshops, phase 3 – white boarding and phase 4 – usability testing. Results The process led to the development and refinement of a smartphone application – Worktivity. The core component was self-monitoring and feedback of SB at work, complemented by additional features focusing on goal setting, prompts and reminders to break up prolonged periods of sitting, and educational facts and tips. Key features of the app included simple data entry and personalisation based on each individual’s self-reported sitting time. Results from the ‘think-aloud’ interviews (n=5) suggest Worktivity was well accepted and that users were positive about its features. Conclusion This study led to the development of Worktivity, a theory-based and user-informed mobile app intervention to reduce occupational SB. It is the first app of its kind developed with the primary aim of reducing occupational SB using digital self-monitoring. This paper provides a template to guide others in the development and evaluation of technology-supported behaviour change interventions.
Objective The aim of the study was to evaluate a technological solution in the form of an App to implement and measure person-centredness in nursing. The focus was to enhance the knowledge transfer of a set of person-centred key performance indicators and the corresponding measurement framework used to inform improvements in the experience of care. Design The study used an evaluation approach derived from the work of the Medical Research Council to assess the feasibility of the App and establish the degree to which the App was meeting the aims set out in the development phase. Evaluation data were collected using focus groups (n = 7) and semi-structured interviews (n = 7) to capture the impact of processes experienced by participating sites. Setting The study was conducted in the UK and Australia in two organizations, across 11 participating sites. Participants 22 nurses from 11 sites in two large health care organizations were recruited on a voluntary basis. Intervention Implementing the KPIs and measurement framework via the APP through two cycles of data collection. Main Outcome Measures The main outcome was to establish feasibility in the use of the App. Results The majority of nurse/midwife participants found the App easy to use. There was broad consensus that the App was an effective method to measure the patient experience and generated clear, concise reports in real time. Conclusions The implementation of the person-centred key performance indicators using the App enhanced the generation of meaningful data to evidence patient experience across a range of different clinical settings.
This paper presents a probabilistic approach for the identification of abnormal behaviour in Activities of Daily Living (ADLs) from sensor data collected from 30 participants. The ADLs considered are":" (i) preparing and drinking tea, and (ii) preparing and drinking coffee. Abnormal behaviour identified in the context of these activities can be an indicator of a progressive health problem or the occurrence of a hazardous incident. The approach presented considers the temporal aspect of the sequences of actions that are part of each ADL and that vary between participants. The average and standard deviation for the durations of each action were calculated to define an average time and a range in which a behaviour could be considered as normal for each stage and activity. The Cumulative Distribution Function (CDF) was used to obtain the probabilities of abnormal behaviours related to the early and late completion of activities and stages within an activity. The data analysis show that CDF can provide accurate and reliable results regarding the presence of abnormal behaviour in stages and activities that last over a minute. Finally, this approach could be used to train machine learning algorithms for the abnormal behaviour detection.
Activity recognition is an important task in many fields, such as ambient intelligence, pervasive healthcare, and surveillance. In particular, the recognition of human gait can be useful to identify the characteristics of the places or physical spaces, such as whether the person is walking on level ground or walking down stairs in which people move. For example, ascending or descending stairs can be a risky activity for older adults because of a possible fall, which can have more severe consequences than if it occurred on a flat surface. While portable and wearable devices have been widely used to detect Activities of Daily Living (ADLs), few research works in the literature have focused on characterizing only actions of human gait. In the present study, a method for recognizing gait activities using acceleration data obtained from a smartphone and a wearable inertial sensor placed on the ankle of people is introduced. The acceleration signals were segmented based on the automatic detection of strides, also called gait cycles. Subsequently, a feature vector of the segmented signals was extracted, which was used to train four classifiers using the Naive Bayes, C4.5, Support Vector Machines, and K-Nearest Neighbors algorithms. Data was collected from seven young subjects who performed five gait activities":" (i) going down an incline, (ii) going up an incline, (iii) walking on level ground, (iv) going down stairs, and (v) going up stairs. The results demonstrate the viability of using the proposed method and technologies in ambient assisted living contexts.
INTRODUCTION Dementia is a syndrome characterised by a decline in memory, language, and problem-solving that affects the ability of patients to perform everyday activities. Patients with dementia tend to experience episodes of anxiety and remain for extended periods, which affects their quality of life. OBJECTIVES To design AnxiDetector, a system capable of detecting patterns of sounds associated before and during the manifestation of anxiety in patients with dementia. METHODS We conducted a non-participatory observation of 70 diagnosed patients in-situ, and conducted semi-structured interviews with four caregivers at a residential centre. Using the findings from our observation and caregiver interviews, we developed the AnxiDetector prototype and tested this in an experimental setting where we defined nine classes of audio to represent two groups of sounds (i) Disturbance which includes audio files that characterise sounds that trigger anxiety in patients with dementia, and (ii) Expression which includes audio files that characterise sounds expressed by the patients during episodes of anxiety. We conducted two experimental classifications of sounds using (i) a Neural Network model trained and (ii) a Support Vector Machine model. The first evaluation consists of a binary discriminating between the two groups of sounds; the second evaluation discriminates the nine classes of audio. The audio resources were retrieved from publicly available datasets. RESULTS The qualitative results present the views of the caregivers on the adoption of AnxiDetector. The quantitative results from our binary discrimination show a classification accuracy of 98.1% and 99.2% for the Deep Neural Network and Support Vector Machine models, respectively. When classifying the nine classes of sound, our model shows a classification accuracy of 92.2%. Whereas, the Support Vector Machine model yielded an overall classification accuracy of 93.0%. CONCLUSION In this paper, we presented the outcomes from an observational study in-site at a residential care centre, qualitative findings from interviews with caregivers, the design of AnxiDetector, and preliminary qualitative results of a methodology devised to detect relevant acoustic events associated with anxiety in patients with dementia. We conclude by signalling future plans to conduct in-situ validation of the effectiveness of AnxiDetector for anxiety detection.
Foot ulcers are a common complication of diabetes and are the leading cause of amputation amongst those with diabetes. Research has shown that, an increase of two degrees Celsius in the skin temperature on the plantar surface of the foot can be an early indication of injury or inflammation. Early detection and treatment of a hotspot region may reduce the risk of an ulcer developing. This paper presents a thermographybased approach for detecting temperature hotspots on the foot. The system comprises a bespoke application and a thermal camera attachment which captures RGB images and a temperature matrix. Web-based services process the captured data and detect whether any regions of higher temperature are present on the foot, in comparison to the other foot. The accuracy of this system has been verified through a pilot study. Hotspots were simulated on the feet of 10 healthy participants. The results indicated that hotspots were correctly detected for 60% of the participants. We discuss some reasons why the results were inaccurate for the remaining four participants. Furthermore, we also suggest some potential enhancements to the system with the aim of increasing the precision of the results.
This paper presents a probabilistic approach for the identification of abnormal behaviour in Activities of Daily Living (ADLs) from dense sensor data collected from 30 participants. The ADLs considered are related to preparing and drinking (i) tea, and (ii) coffee. Abnormal behaviour identified in the context of these activities can be an indicator of a progressive health problem or the occurrence of a hazardous incident. The approach presented considers the temporal and sequential aspects of the actions that are part of each ADL and that vary between participants. The average and standard deviation for the duration and number of steps of each activity are calculated to define the average time and steps and a range within which a behaviour could be considered as normal for each stage and activity. The Cumulative Distribution Function (CDF) is used to obtain the probabilities of abnormal behaviours related to the early and late completion of activities and stages within an activity in terms of time and steps. Analysis shows that CDF can provide precise and reliable results regarding the presence of abnormal behaviour in stages and activities that last over a minute or consist of many steps. Finally, this approach could be used to train machine learning algorithms for abnormal behaviour detection.