Adaptive probability scheme for behaviour monitoring of the elderly using a specialised ambient device

A Hidden Markov Model (HMM) modified to work in combination with a Fuzzy System is utilised to determine the current behavioural state of the user from information obtained with specialised hardware. Due to the high dimensionality and not-linearly-separable nature of the Fuzzy System and the sensor data obtained with the hardware which informs the state decision, a new method is devised to update the HMM and replace the initial Fuzzy System such that subsequent state decisions are based on the most recent information. The resultant system first reduces the dimensionality of the original information by using a manifold representation in the high dimension which is unfolded in the lower dimension. The data is then linearly separable in the lower dimension where a simple linear classifier, such as the perceptron used here, is applied to determine the probability of the observations belonging to a state. Experiments using the new system verify its applicability in a real scenario

Dimension reduction for linear separation with curvilinear distances

Any high dimensional data in its original raw form may contain obviously classifiable clusters which are difficult to identify given the high-dimension representation. In reducing the dimensions it may be possible to perform a simple classification technique to extract this cluster information whilst retaining the overall topology of the data set. The supervised method presented here takes a high dimension data set consisting of multiple clusters and employs curvilinear distance as a relation between points, projecting in a lower dimension according to this relationship. This representation allows for linear separation of the non-separable high dimensional cluster data and the classification to a cluster of any successive unseen data point extracted from the same higher dimension

Behaviour recognition and monitoring of the elderly using wearable wireless sensors. Dynamic behaviour modelling and nonlinear classification methods and implementation.

In partnership with iMonSys - an emerging company in the passive care field - a new system, 'Verity', is being developed to fulfil the role of a passive behaviour monitoring and alert detection device, providing an unobtrusive level of care and assessing an individual's changing behaviour and health status whilst still allowing for independence of its elderly user. In this research, a Hidden Markov Model incorporating Fuzzy Logic-based sensor fusion is created for the behaviour detection within Verity, with a method of Fuzzy-Rule induction designed for the system's adaptation to a user during operation. A dimension reduction and classification scheme utilising Curvilinear Distance Analysis is further developed to deal with the recognition task presented by increasingly nonlinear and high dimension sensor readings, and anomaly detection methods situated within the Hidden Markov Model provide possible solutions to identification of health concerns arising from independent living. Real-time implementation is proposed through development of an Instance Based Learning approach in combination with a Bloom Filter, speeding up the classification operation and reducing the storage requirements for the considerable amount of observation data obtained during operation. Finally, evaluation of all algorithms is completed using a simulation of the Verity system with which the behaviour monitoring task is to be achieved

An intelligent information forwarder for healthcare big data systems with distributed wearable sensors

© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed

John Brown, the hero; personal reminiscences,

Mode of access: Internet

Do the effects of psychological treatments on improving glycemic control in type 1 diabetes persist over time? A long-term follow-up of a randomized controlled trial.

OBJECTIVES: In a randomized controlled trial, adults with Type 1 diabetes and suboptimal glycemic control who received motivational enhancement therapy (MET) plus cognitive behavioral therapy (CBT) had a greater reduction in their 12-month hemoglobin A(1c) (Hb(A1c)) than those who received usual care (UC). We tested whether improvements in glycemic control persisted up to 4 years after randomization. METHODS: In the original trial, participants were randomized to UC (n = 121), 4 sessions of MET (n = 117), or 4 sessions of MET plus 8 sessions of CBT (n = 106). Of the 344 patients who participated in the original trial, 260 (75.6%) consented to take part in this posttrial study. A linear mixed model was fitted to available measurements to assess whether intervention effects on Hb(A1c) at 12 months were sustained at 2, 3, and 4 years. RESULTS: Estimated mean Hb(A1c) level was lower for participants in the two intervention arms when compared with UC at 2, 3, and 4 years, but none of the differences were statistically significant. At 4 years, estimated mean Hb(A1c) level for MET plus CBT was 0.28% (95% confidence interval = -0.22% to 0.77%) lower than that for UC, and estimated mean Hb(A1c) level for MET was 0.17% (95% confidence interval = -0.33% to 0.66%) lower than that for UC. CONCLUSIONS: There was no evidence of benefit for patients randomized to MET plus CBT at 2, 3, or 4 years. Larger studies are needed to estimate long-term treatment effects with greater precision. Current models of psychological treatments in diabetes may need to be intensified or include maintenance sessions to maintain improvements in glycemic control