Multi-sensor activity recognition of an elderly person.

The rapid increase in the number of ageing population brings major issues to health care including a rise in care cost, high demand in long- term care, burden to caregivers, and insufficient and ineffective care. Activity recognition can be used as the key part of the intelligent sys- tems to allow elderly people to live independently at homes, reduce care cost and burden to the caregivers, provide assurance for the fam- ilies, and promote better care. However, current activity recognition systems mainly focus on the technical aspect i.e. systems accuracy and neglects the practical aspects such as acceptance, usability, cost and privacy. The practicality of the system is the vital indication whether the system will be adopted. This research aims to develop the activity recognition system which considers both practical and technical aspects using multiple wrist-worn sensors. An extensive literature review in wearable sensor based activity recog- nition and its applications in healthcare have been carried out. Novel multi-sensor activity recognition utilising multiple low-cost, non-intrusive, non-visual wearable sensors is proposed. The sensor fusion is per- formed at feature and classi er levels using the proposed feature se- lection and classi er combination techniques. The multi-sensor ac- tivity recognition data sets have been collected. The rst data set contains data from accelerometer collected from seven young adults. The second data set contains data from accelerometer, altimeter, and temperature sensor collected from 12 elderly people in home environ- ment performing 10 activities. The third data set contains sensor data from accelerometer, gyroscope, temperature sensor, altimeter, barometer, and light sensor worn on the users wrist and a heart rate monitor worn over the users chest. The data set is collected from 12 elderly persons in a real home environment performing 13 activities. This research proposes two feature selection methods, Feature Com- bination (FC) and Maximal Relevancy and Maximal Complementary (MRMC), based on the relationship between feature and classes as well as the relationship between a group of features and classes. The experimental studies show that the proposed techniques can select an optimum set of features from irrelevant, overlapped, and partly over- lapped features. The studies also show that FC and MRMC obtain higher classi cation performances than popular techniques including MRMR, NMIFS, and Clamping. Two classi er combination tech- niques based on Genetic Algorithm (GA) are proposed. The rst technique called GA based Fusion Weight (GAFW), uses GA nd the optimum fusion weights. The results indicate that 99% of classi er fusion using GAFW achieves equal or higher accuracy than using only the best classi er. While other fusion weight techniques cannot guar- antee accuracy improvement, GAFW is a more suitable method for determining fusion weight regardless which fusion techniques are used. Another algorithm called GA based Combination Model (GACM) is proposed to nd the optimal combination between classi er, weight function, and classi er combiners. The algorithm does not only nd the model which has the minimum classi cation error but also select the one that is simpler. Other criteria e.g. select the classi er with low computation can also be easily added to the algorithm. The re- sults show that in general GACM can nd the optimum combinations automatically. The comparison against manually selection revealed that there is no statistical signi cant in the performances. Applications of the proposed work in home care and decision support system are discussed The results of this research will have a signi cant impact on the future health care where people can be health monitored from their homes to promote healthy living, detect any changes in behaviour, and improve quality of care

A practical multi-sensor activity recognition system for home-based care

To cope with the increasing number of aging population, a type of care which can help prevent or postpone entry into institutional care is preferable. Activity recognition can be used for home-based care in order to help elderly people to remain at home as long as possible. This paper proposes a practical multi-sensor activity recognition system for home-based care utilizing on-body sensors. Seven types of sensors are investigated on their contributions toward activity classification. We collected a real data set through the experiments participated by a group of elderly people. Seven classification models are developed to explore contribution of each sensor. We conduct a comparison study of four feature selection techniques using the developed models and the collected data. The experimental results show our proposed system is superior to previous works achieving 97% accuracy. The study also demonstrates how the developed activity recognition model can be applied to promote a home-based care and enhance decision support system in health care

Machine learning can predict disease manifestations and outcomes in lymphangioleiomyomatosis

Background LAM is a rare multisystem disease with variable clinical manifestations and differing rates of progression that make management decisions and giving prognostic advice difficult. We used machine learning to identify clusters of associated features which could be used to stratify patients and predict outcomes in individuals.Patients and methods Using unsupervised machine learning we generated patient clusters using data from 173 women with LAM from the UK and 186 replication subjects from the NHLBI LAM registry. Prospective outcomes were associated with cluster results.Results Two and three-cluster models were developed. A three-cluster model separated a large group of subjects presenting with dyspnoea or pneumothorax from a second cluster with a high prevalence of angiomyolipoma symptoms (p=0.0001) and TSC (p=0.041). The third cluster were older, never presented with dyspnoea or pneumothorax (p=0.0001) and had better lung function. Similar clusters were reproduced in the NHLBI cohort. Assigning patients to clusters predicted prospective outcomes: in a two-cluster model future risk of pneumothorax was 3.3 fold (95% C.I. 1.7–5.6) greater in cluster one than two (p=0.0002). Using the three-cluster model, the need for intervention for angiomyolipoma was lower in clusters two and three than cluster one (

The BCD Triage Sieve outperforms all existing major incident triage tools:comparative analysis using the UK national trauma registry population

BACKGROUND: Natural disasters, conflict, and terrorism are major global causes of death and disability. Central to the healthcare response is triage, vital to ensure the right care is provided to the right patient at the right time. The ideal triage tool has high sensitivity for the highest priority (P1) patients with acceptably low over-triage. This study compared the performance of major incident triage tools in predicting P1 casualty status in adults in the prospective UK Trauma Audit and Research Network (TARN) registry. METHODS: TARN patients aged 16+ years (January 2008-December 2017) were included. Ten existing triage tools were applied using patients’ first recorded pre-hospital physiology. Patients were subsequently assigned triage categories (P1, P2, P3, Expectant or Dead) based on pre-defined, intervention-based criteria. Tool performance was assessed by comparing tool-predicted and intervention-based priority status. FINDINGS: 195,709 patients were included; mortality was 7·0% (n=13,601); median Injury Severity Score (ISS) was 9 (IQR 9–17); 97·1% sustained blunt injuries. 22,144 (11·3%) patients fulfilled intervention-based criteria for P1 status, exhibiting higher mortality (12·8% vs. 5·0%, p<0.001), increased intensive care requirement (52·4% vs 5·0%, p<0.001), and more severe injuries (median ISS 21 vs 9, p<0.001) compared with P2 patients. In 16–64 year olds, the highest performing tool was the Battlefield Casualty Drills (BCD) Triage Sieve (Prediction of P1 status: 70·4% sensitivity, over-triage 70·9%, area under the receiver operating curve (AUC) 0·068 [95%CI 0·676–0·684]). The UK National Ambulance Resilience Unit (NARU) Triage Sieve had sensitivity of 44·9%; over-triage 56·4%; AUC 0·666 (95%CI 0·662–0·670). All tools performed poorly amongst the elderly (65+ years). INTERPRETATION: The BCD Triage Sieve performed best in this nationally representative population; we recommend it supersede the NARU Triage Sieve as the UK primary major incident triage tool. Validated triage category definitions are recommended for appraising future major incidents. FUNDING: This study is funded by the National Institute for Health Research (NIHR) Surgical Reconstruction and Microbiology Research Centre. GVG also acknowledges support from the MRC Heath Data Research UK (HDRUK/CFC/01). The views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care, or the Ministry of Defence

Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis

Background: Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess multiple and higher-dimension interactions of comorbidities, and define clusters of β-blocker efficacy in patients with sinus rhythm and atrial fibrillation. Methods: Neural network-based variational autoencoders and hierarchical clustering were applied to pooled individual patient data from nine double-blind, randomised, placebo-controlled trials of β blockers. All-cause mortality during median 1·3 years of follow-up was assessed by intention to treat, stratified by electrocardiographic heart rhythm. The number of clusters and dimensions was determined objectively, with results validated using a leave-one-trial-out approach. This study was prospectively registered with ClinicalTrials.gov (NCT00832442) and the PROSPERO database of systematic reviews (CRD42014010012). Findings: 15 659 patients with heart failure and LVEF of less than 50% were included, with median age 65 years (IQR 56–72) and LVEF 27% (IQR 21–33). 3708 (24%) patients were women. In sinus rhythm (n=12 822), most clusters demonstrated a consistent overall mortality benefit from β blockers, with odds ratios (ORs) ranging from 0·54 to 0·74. One cluster in sinus rhythm of older patients with less severe symptoms showed no significant efficacy (OR 0·86, 95% CI 0·67–1·10; p=0·22). In atrial fibrillation (n=2837), four of five clusters were consistent with the overall neutral effect of β blockers versus placebo (OR 0·92, 0·77–1·10; p=0·37). One cluster of younger atrial fibrillation patients at lower mortality risk but similar LVEF to average had a statistically significant reduction in mortality with β blockers (OR 0·57, 0·35–0·93; p=0·023). The robustness and consistency of clustering was confirmed for all models (p<0·0001 vs random), and cluster membership was externally validated across the nine independent trials. Interpretation: An artificial intelligence-based clustering approach was able to distinguish prognostic response from β blockers in patients with heart failure and reduced LVEF. This included patients in sinus rhythm with suboptimal efficacy, as well as a cluster of patients with atrial fibrillation where β blockers did reduce mortality

Perception of Smart Home Technologies to Assist Elderly People

In the last decade, the number of elderly population has increased significantly which affects human in many aspects, especially in healthcare. Many studies have shown increases in expenditures on longterm care. New models of care are needed including supported self-care and home-based services. Advance in sensor and network technologies have made these possible. A smart home which is a residence equipped with smart technologies providing services that enhance human way of living i.e. safety, security, entertainment, etc would allow elderly to maintain living independently in their homes and still in control of their healthcare cost and status. This paper reviews various topics on smart home technologies including smart home projects, smart home network, smart home appliance and sensor technologies for smart home. A successful adoption of smart home technologies requires appreciation of stakeholders’ perceptions, needs and concerns. A survey has been carried out at a major hospital, nursing homes and general population to explore the perception of six smart home technologies to assist elderly people and concerns regarding the use of smart home technologies. Overall, the result showed positive feedbacks toward these technologies. Participants were concerned about issues such as lack of human responders, user friendliness of the device and the need of learning new technology. In terms of willingness of adoption of these technologies is still unclear. However, it is suggested that by giving real experiences of smart home technologies, their usefulness and effectiveness would be more appreciated

Genetic Algorithm-Based Classifiers Fusion for Multisensor Activity Recognition of Elderly People

Activity recognition of an elderly person can be used to provide information and intelligent services to health care professionals, carers, elderly people, and their families so that the elderly people can remain at homes independently. This study investigates the use and contribution of wrist-worn multisensors for activity recognition. We found that accelerometers are the most important sensors and heart rate data can be used to boost classification of activities with diverse heart rates. We propose a genetic algorithm-based fusion weight selection (GAFW) approach which utilizes GA to find fusion weights. For all possible classifier combinations and fusion methods, the study shows that 98% of times GAFW can achieve equal or higher accuracy than the best classifier within the group

Maximum relevancy maximum complementary feature selection for multi-sensor activity recognition

We propose a feature selection algorithm using MRMC.Show that MRMC provides a good result comparing to the 3 popular algorithms.The complementary measure improves the performance of the Clamping algorithm.Evaluate the proposed algorithm on 2 well-defined problems and 5 real life data sets. In the multi-sensor activity recognition domain, the input space is often large and contains irrelevant and overlapped features. It is important to perform feature selection in order to select the smallest number of features which can describe the outputs. This paper proposes a new feature selection algorithms using the maximal relevance and maximal complementary (MRMC) based on neural networks. Unlike other feature selection algorithms that are based on relevance and redundancy measurements, the idea of how a feature complements to the already selected features is utilized. The proposed algorithm is evaluated on two well-defined problems and five real world data sets. The data sets cover different types of data i.e. real, integer and category and sizes i.e. small to large set of features. The experimental results show that the MRMC can select a smaller number of features while achieving good results. The proposed algorithm can be applied to any type of data, and demonstrate great potential for the data set with a large number of feature

Elderly activities recognition and classification for applications in assisted living

Assisted living systems can help support elderly persons with their daily activities in order to help them maintain healthy and safety while living independently. However, most current systems are ineffective in actual situation, difficult to use and have a low acceptance rate. There is a need for an assisted living solution to become intelligent and also practical issues such as user acceptance and usability need to be resolved in order to truly assist elderly people. Small, inexpensive and low-powered consumption sensors are now available which can be used in assisted living applications to provide sensitive and responsive services based on users current environments and situations. This paper aims to address the issue of how to develop an activity recognition method for a practical assisted living system in term of user acceptance, privacy (non-visual) and cost. The paper proposes an activity recognition and classification method for detection of Activities of Daily Livings (ADLs) of an elderly person using small, low-cost, non-intrusive non-stigmatize wrist worn sensors. Experimental results demonstrate that the proposed method can achieve a high classification rate (>90%). Statistical tests are employed to support this high classification rate of the proposed method. Also, we prove that by combining data from temperature sensor and/or altimeter with accelerometer, classification accuracy can be improved