James D. Amor

Preliminary study on activity monitoring using an android smart-watch.

The global trend for increasing life expectancy is resulting in aging populations in a number of countries. This brings to bear a pressure to provide effective care for the older population with increasing constraints on available resources. Providing care for and maintaining the independence of an older person in their own home is one way that this problem can be addressed. The EU Funded Unobtrusive Smart Environments for Independent Living (USEFIL) project is an assistive technology tool being developed to enhance independent living. As part of USEFIL, a wrist wearable unit (WWU) is being developed to monitor the physical activity (PA) of the user and integrate with the USEFIL system. The WWU is a novel application of an existing technology to the assisted living problem domain. It combines existing technologies and new algorithms to extract PA parameters for activity monitoring. The parameters that are extracted include: activity level, step count and worn state. The WWU, the algorithms that have been developed and a preliminary validation are presented. The results show that activity level can be successfully extracted, that worn state can be correctly identified and that step counts in walking data can be estimated within 3% error, using the controlled dataset.

Personalised Ambient Monitoring (PAM) of the mentally ill

One in ten of the (UK) population will suffer a disabling mental disorder at some stage in their life. Bipolar disorder is one such illness and is characterized by periods of depression or manic activity interspersed with stretches of normality. Some patients are able to manage this condition via their self-awareness that enables them to detect the onset of debilitating episodes and so take effective action. Such self management can be achieved through a paper-based process, although more recently PDAs have been used with success. This presentation will introduce the Personalised Ambient Monitoring (PAM) concept that aims to augment such processes by automatically providing and merging environmental details and information relating to personal activity. Essentially the PAM project is investigating what may be loosely referred to as ‘electronic’ monitoring to automatically record ‘activity signatures’ and subsequently use this data to issue alerts. The types of data that we are considering using includes: location and activity (e.g. via GPS and accelerometers); and environment (e.g. temperature and light levels). Other types of sensor under consideration are passive IR sensors (within the home); and sound processing to log the audio ‘environment’. The use of such monitoring will be agreed between the patient and their health care team and it is anticipated that different patients will be comfortable with different sensor packages, thus personalizing the monitoring. Although such tele-monitoring is now generally common, its use in the treatment of the mentally ill is still in its infancy. This paper will consider the specific problems faced in applying it to this community along with the aims of this project. In addition, the use of modelling to predict the effects of the possible problems of sparse data that is expected, and to predict the effect on the overall patient pathway will be considered.

Assistive technology to monitor activity, health and wellbeing in old age: The wrist wearable unit in the USEFIL project

This paper presents the assistive technology used to perform activity monitoring in the USEFIL (Unobtrusive Smart Environments for Independent Living) project, particularly the wrist wearable unit. USEFIL includes a number of activity monitoring devices alongside some condition specific medical devices, a dedicated electronic health record database and communication backend. The system is designed as an assistive technology to provide long-term monitoring for older people in their own home and communicate the data that is gathered into a decision support system that can be used by the older persons carers to improve their care and allow them to remain independent in their own home. The wrist wearable device developed for the USEFIL project, the various health indicators extracted from its inbuilt sensors and how these are used to understand the health and wellbeing of the older person are discussed in this paper.

Detecting and Analyzing Activity Levels for the Wrist Wearable Unit in the USEFIL Project

Aging populations present a new set of health challenges. In particular the need to provide evermore healthcare with relatively fewer resources is becoming an increasing issue. This particular problem is highly suited to a technological approach and in this paper we present the USEFIL project and examine how wearable devices can be used to enhance independent living for older people. We present the work that has been undertaken to date on the development of a wrist wearable unit that can capture the user’s physical activity. Initial steps in the processing of this accelerometry data to determine a score for the user’s level of physical activity in one minute increments are described and the results from this processing are shown.

Characterization of wrist-wearable activity measurement using whole body calorimetry in semi-free living conditions.

Physical activity (PA) is a significant factor in a number of health conditions and monitoring PA can play a significant role in the treatment of, or research into, these conditions. For longitudinal monitoring of PA, unobtrusive devices are often used and there is a need for the development of energy expenditure (EE) estimation techniques from single-device systems. This paper presents an experiment designed to characterize the relationship between a previously described technique, the activity score (AS) and EE obtained from whole-room indirect calorimetry. The study used 8 participants over a 24-hr period with interspersed exercise periods to observe physical movement with wearable devices and EE in 5 minute epochs. Results show that AS and EE are correlated with a Spearmans rank correlation coefficient of 0.775 with p <; 0.001.

Wrist-Worn Accelerometer to Detect Postural Transitions and Walking Patterns

The identification of postural transitions and walking patterns are important in the recognition of activities of daily living. This paper presents a feasibility study to see if postural transitions and walking patterns can be identified using a wrist-worn accelerometer-based device. Firstly, the postural transition (i.e. sit-to-stand and stand-to-sit) templates are extracted and template matching is used to identify the transitions between daily living activities such as walking within a house. Secondly, the various walking patterns such as walking on a level surface, walking up the stairs and walking down the stairs are classified using statistical features and nearest neighbor classifier. A comparison between the activity pattern from the dominant wrist and the non-dominant wrist is also presented.

A multi-state model to improve the design of an automated system to monitor the activity patterns of patients with bipolar disorder

This paper describes the role of mathematical modelling in the design and evaluation of an automated system of wearable and environmental sensors called PAM (Personalised Ambient Monitoring) to monitor the activity patterns of patients with bipolar disorder (BD). The modelling work was part of an EPSRC-funded project, also involving biomedical engineers and computer scientists, to develop a prototype PAM system. BD is a chronic, disabling mental illness associated with recurrent severe episodes of mania and depression, interspersed with periods of remission. Early detection of the onset of an acute episode is crucial for effective treatment and control. The aim of PAM is to enable patients with BD to self-manage their condition, by identifying the persons normal ‘activity signature’ and thus automatically detecting tiny changes in behaviour patterns which could herald the possible onset of an acute episode. PAM then alerts the patient to take appropriate action in time to prevent further deterioration and possible hospitalisation. A disease state transition model for BD was developed, using data from the clinical literature, and then used stochastically in a Monte Carlo simulation to test a wide range of monitoring scenarios. The minimum best set of sensors suitable to detect the onset of acute episodes (of both mania and depression) is identified, and the performance of the PAM system evaluated for a range of personalised choices of sensors.

Setting the scene: Mobile and wearable technology for managing healthcare and wellbeing

The growing proliferation of mobile and wearable technology (MWT) offers interesting use cases when applied to health and wellness management. Current trends towards more longer term health and wellness management coupled with global challenges around the provision of healthcare to aging populations with tighter budget constraints, create rich opportunities to exploit this new technology to maintain health and wellness. This paper provides an overview of commonly available MWT and examines how it can be used in health and wellness systems. Case studies are given from two recent research projects and the issues and challenges that arise in the use of MWT are discussed. We conclude that MWT offers some key advantages in some healthcare situations, but that care must be taken to select appropriate technology for each use.

Behavioral pattern detection from Personalized Ambient Monitoring

Bipolar disorder (BD) is a serious psychiatric condition that affects a large number of people. Many people with BD self-monitor their condition in order to try and keep the disturbances from affective episodes to a minimum. The Personalized Ambient Monitoring (PAM) project has developed a system that performs behavioral monitoring in an unobtrusive manner and can detect changes in a persons behavior. The system uses a variety of discreet sensors to gather data on the parsons behavior and this data is processed to extract behavioral patterns and detect changes in those patterns. In this paper we present one method of data processing that takes 24hr long data-streams from the sensors, pre-processes them and uses the Continuous Profile Model to align and extract the underlying patterns from the data-streams. We present some preliminary results from a technical trial.

AART-BC: A sensor system for monitoring Assistive Technology use beyond the clinic

A wide range of assistive and rehabilitative technologies (ART) are available to assist with mobility and upper limb function. However, anecdotal evidence suggests many of the devices prescribed, or purchased, are either poorly used, or rejected entirely. This situation is costly, both for the healthcare provider and the user, and may be leading to secondary consequences, such as falls and/or social isolation. This paper reports on the development and initial feasibility testing of a system for monitoring when and how assistive devices are used outside of the clinic setting, and feeding this information to the device user themselves and/or prescribing clinician (where appropriate). Illustrative data from multiple time-synchronized device and body worn sensors are presented on a wheelchair user and a user of a “rollator” walking frame, moving along a walkway. Observation of the sensor data in both cases showed characteristic signatures corresponding to individual “pushes”. In parallel with this work, other project partners are exploring clinician and patient data requirements, as well we sensor set acceptability The initial results highlight the potential for the approach and demonstrate the need for further work to reduce and optimize the sensor set.