Employing multi-modal sensors for personalised smart home health monitoring.

Smart home systems are employed worldwide for a variety of automated monitoring tasks. FITsense is a system that performs personalised smart home health monitoring using sensor data. In this thesis, we expand upon this system by identifying the limits of health monitoring using simple IoT sensors, and establishing deployable solutions for new rich sensing technologies. The FITsense system collects data from FitHomes and generates behavioural insights for health monitoring. To allow the system to expand to arbitrary home layouts, sensing applications must be delivered while relying on sparse "ground truth" data. An enhanced data representation was tested for improving activity recognition performance by encoding observed temporal dependencies. Experiments showed an improvement in activity recognition accuracy over baseline data representations with standard classifiers. Channel State Information (CSI) was chosen as our rich sensing technology for its ambient nature and potential deployability. We developed a novel Python toolkit, called CSIKit, to handle various CSI software implementations, including automatic detection for off-the-shelf CSI formats. Previous researchers proposed a method to address AGC effects on COTS CSI hardware, which we tested and found to improve correlation with a baseline without AGC. This implementation was included in the public release of CSIKit. Two sensing applications were delivered using CSIKit to demonstrate its functionality. Our statistical approach to motion detection with CSI data showed a 32% increase in accuracy over an infrared sensor-based solution using data from 2 unique environments. We also demonstrated the first CSI activity recognition application on a Raspberry Pi 4, which achieved an accuracy of 92% with 11 activity classes. An application was then trained to support movement detection using data from all COTS CSI hardware. This was combined with our signal divider implementation to compare CSI wireless and sensing performance characteristics. The IWL5300 exhibited the most consistent wireless performance, while the ESP32 was found to produce viable CSI data for sensing applications. This establishes the ESP32 as a low-cost high-value hardware solution for CSI sensing. To complete this work, an in-home study was performed using real-world sensor data. An ESP32-based CSI sensor was developed to be integrated into our IoT network. This sensor was tested in a FitHome environment to identify how the data from our existing simple sensors could aid sensor development. We performed an experiment to demonstrate that annotations for CSI data could be gathered with infrared motion sensors. Results showed that our new CSI sensor collected real-world data of similar utility to that collected manually in a controlled environment

Employing multi-modal sensors for personalised smart home health monitoring.

As the prevalence of IoT sensor equipment in smart homes continues to rise, long term monitoring for personalised and more representative health tracking has become more accessible. The estimation of physiological health factors such as gait and heart rate can be captured using a range of diverse sensor equipment, while behavioural changes are now being monitored using simple binary sensors through activity classification and profiling. Combining both physiological and behavioural monitoring in fixed layout properties has already allowed us to effectively consider fall risk. However, expanding application of the system to new layouts and conditions requires consideration of differing retro fit home layouts and sensor configurations. A wider selection of sensors in varying configurations could potentially allow for the identification of other health conditions such as heart disease and stroke

Fall prediction using behavioural modelling from sensor data in smart homes.

The number of methods for identifying potential fall risk is growing as the rate of elderly fallers continues to rise in the UK. Assessments for identifying risk of falling are usually performed in hospitals and other laboratory environments, however these are costly and cause inconvenience for the subject and health services. Replacing these intrusive testing methods with a passive in-home monitoring solution would provide a less time-consuming and cheaper alternative. As sensors become more readily available, machine learning models can be applied to the large amount of data they produce. This can support activity recognition, falls detection, prediction and risk determination. In this review, the growing complexity of sensor data, the required analysis, and the machine learning techniques used to determine risk of falling are explored. The current research on using passive monitoring in the home is discussed, while the viability of active monitoring using vision-based and wearable sensors is considered. Methods of fall detection, prediction and risk determination are then compared

Wifi-based human activity recognition using Raspberry Pi.

Ambient, non-intrusive approaches to smart home health monitoring, while limited in capability, are preferred by residents. More intrusive methods of sensing, such as video and wearables, can offer richer data but at the cost of lower resident uptake, in part due to privacy concerns. A radio frequency-based approach to sensing, Channel State Information (CSI),can make use of low cost off-the-shelf WiFi hardware. We have implemented an activity recognition system on the Raspberry Pi 4, one of the world’s most popular embedded boards. We have implemented an classification system using the Pi to demonstrate its capability for activity recognition. This involves performing data collection, interpretation and windowing, before supplying the data to a classification model. In this paper, the capabilities of the Raspberry Pi 4 at performing activity recognition on CSI data are investigated. We have developed and publicly released a data interaction framework, capable of interpreting, processing and visualising data from a range of CSI-capable hardware. Furthermore, CSI data captured for these experiments during various activity performances have also been made publically available. We then train a Deep Convolutional LSTM model to classify the activities. Our experiments, performed in a small apartment, achieve 92% average accuracy on 11 activity classes

A qualitative study of the experiences of insulin use by older people with type 2 diabetes mellitus

Background: There is a rising prevalence of type 2 diabetes among older people. This population also suffers from co-morbidity and a greater number of diabetes related complications, such as visual and cognitive impairment, which can potentially affect their ability to manage insulin regimens. Understanding the experiences of older people when they transition to insulin will help the development of healthcare interventions to enhance their diabetes outcomes, overall health and quality of life.Aims: The aims of this exploratory study were to 1) understand the experiences of older people with type 2 diabetes in relation to insulin treatment initiation and management and 2) use this understanding to consider how the insulin management support provided to older people by healthcare providers could be more tailored to their needs.Method: A qualitative study using semi structured (remote) interviews with older people with diabetes (n=10) and caregivers (n=4) from the UK. Interviews were audio recorded and transcribed, and framework analysis was used to analyse the data.Results: Three main themes, along with six subthemes, were generated from the study data. Participants generally felt at ease with insulin administration following training, yet some reported feelings of failure at transitioning to insulin use. Participants were also frustrated at what they perceived were insufficient resources for effective self-management, coupled with a lack of professional interest in optimising their health as older people. Some also expressed dissatisfaction regarding the brevity of their consultations, inconsistent information from different healthcare professionals and poor treatment coordination between primary and secondary care. Conclusion: Overall, the study emphasised that older people need better support, education and resources to help manage their insulin use. Healthcare professionals should be encouraged to adopt a more individualised approach to supporting older people that acknowledges their prior knowledge, physical and psychological capabilities and motivation for diabetes self-management. In addition, better communication between different services and greater access to specialist support is clearly needed for this older population. Practice implications: An integrated care pathway for insulin use in older people could be considered. This would include an assessment of the older person’s needs and capacity on their initiation to insulin; targeted education and training in self-management; timely access to appropriate emotional and peer support resources; care plans developed collaboratively with patients; and individualised glucose targets that recognise the needs and preferences of the older person.<br/

Visualisation to explain personal health trends in smart homes.

An ambient sensor network is installed in Smart Homes to identify low-level events taking place by residents, which are then analysed to generate a profile of activities of daily living. These profiles are compared to both the resident's typical profile and to known 'risky' profiles to support recommendation of evidence-based interventions. Maintaining trust presents an XAI challenge because the recommendations are not easily interpretable. Trust in the system can be improved by making the decision-making process more transparent. We propose a visualisation workflow which presents the data in clear, colour-coded graphs

Illinois State University Chamber Orchestra:Stories for Children

Center for the Performing Arts Thursday Evening November 6, 2003 6:00p.m

Representing temporal dependencies in human activity recognition.

Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident’s behaviour can be produced from sensor data, and then compared over time. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs and consider the temporal dependencies that exist in binary ambient sensor data in order to produce case-based representations. These LSTM approaches are compared to the performance of a selection of baseline classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier