Identifying Users with Wearable Sensors based on Activity Patterns

We live in a world where ubiquitous systems surround us in the form of automated homes, smart appliances and wearable devices. These ubiquitous systems not only enhance productivity but can also provide assistance given a variety of different scenarios. However, these systems are vulnerable to the risk of unauthorized access, hence the ability to authenticate the end-user seamlessly and securely is important. This paper presents an approach for user identification given the physical activity patterns captured using on-body wearable sensors, such as accelerometer, gyroscope, and magnetometer. Three machine learning classifiers have been used to discover the activity patterns of users given the data captured from wearable sensors. The recognition results prove that the proposed scheme can effectively recognize a user’s identity based on his/her daily living physical activity patterns

Activities of daily life recognition using process representation modelling to support intention analysis

Purpose – This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge. Design/methodology/approach – This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer’s patients. Findings – A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches. Originality/value – The work presented in this paper is novel, as the developed ADL recognition approach takes into account all relationships and dependencies within the modelled ADLs. This is very useful when conducting activity recognition with very limited features

Recognizing activities of daily living from patterns and extraction of web knowledge

The ability to infer and anticipate the activities of elderly individuals with cognitive impairment has made it possible to provide timely assistance and support, which in turn allows them to lead an independent life. Traditional non-intrusive activity recognition approaches are dependent on the use of various machine learning techniques to infer activities given the collected object usage data. Current activity recognition approaches are also based on knowledge driven techniques that require extensive modelling of the activities that needs to be inferred. These models can be seen as too restrictive, prescriptive and static as they are based on a finite set of activities. In this paper, we propose a novel “top down” approach to recognising activities based on object usage data, which detects patterns associated with the activity-object relationship and utilizes web knowledge in order to build dynamic activity models based on the objects used to perform the activity. Experimental results using the Kasteren dataset shows it is comparable to existing approaches

Taskification – Gamification of Tasks

Leading a busy lifestyle can have a negative impact on the productivity levels of individuals. Lack of motivation is also another factor that can influence the output of any task or activity conducted by a user. This also applies to students within an academic context, where the distractions and lack of motivation can have a negative impact on their learning and results. In this paper, we propose ‘Taskification’, a task management mobile application, which incorporates core gamification features. The objective of this application is to increase student engagement and motivation during tasks such as coursework or exam preparation

Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey

Pattern recognition is seen as a major challenge within the field of data mining and knowledge discovery. For the work in this paper, we have analyzed a range of widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. This study also focuses on each of the algorithm’s strengths and weaknesses for finding patterns among large item sets in database systems

A Framework to Recognise Daily Life Activities with Wireless Proximity and Object Usage Data

The profusion of wireless enabled mobile devices in daily life routine and advancement in pervasive computing has opened new horizons to analyse and model the contextual information. This contextual information (for example, proximity data and location information) can be very helpful in analysing the human behaviours. Wireless proximity data can provide important information about the behaviour and daily life routines of an individual. In this paper, we used Bluetooth proximity data to validate this concept by detecting repeated activity patterns and behaviour of low entropy mobile people by using n-gram and correlative matrix techniques. Primary purpose is to find out whether contextual information obtained from Bluetooth proximity data is useful for activities and behaviour detection of individuals. Repeated patterns found in Bluetooth proximity data can also show the long term routines such as, monthly or yearly patterns in an individual's daily life that can further help to analyse more complex and abnormal routines of human behaviour

Behavioural Patterns Analysis of Low Entropy People Using Proximity Data

Over the years, wireless enabled mobile devices have become an important part of our daily activities that can provide rich contextual information about the location and environment of an individual (for example who is in your proximity? and where are you?). Advancement in technology has opened several horizons to analyse and model this contextual information for human behaviour understanding. Objective of this research work is to utilise this information from wireless proximity data to find repeated patterns in daily life activities and individual behaviours. These repeated patterns can give information about the unusual activities and behaviour of an individual. To validate and further investigate this concept, we used Bluetooth proximity data in this paper. Repeated activity patterns and behaviour of low entropy mobile people are detected by using two different techniques, N-gram and correlative matrix techniques. Primary purpose was to find out whether contextual information obtained from Bluetooth proximity data is useful for activities and behaviour detection of individuals. Results have shown that these repeated patterns not only show short term daily routines but can also show the long term routines such as, monthly or yearly patterns in an individual’s daily life that can further help to analyse more complex and abnormal routines of human behaviour

Toward food waste reduction at universities

Food waste is a serious problem, which undermines the achievement of many sustainable development goals (SDGs), despite their consideration in the agendas of many countries and companies. Notoriously, food waste (FW) causes different kinds of pollution that affect public health and social justice, while contributing to economic losses. This waste phenomenon has causes, drivers, and impacts that require rigorous assessments and effective approaches to mitigate its noxious effects, which are a serious concern for universities. Within these institutions, reducing food waste becomes a circular economy strategy, which is being utilized to assist in promoting sustainable development. However, there is a need for urgent attention to the specific causes of food waste and for consistent actions to reduce it, while boosting awareness in the campus community and triggering a change in students’ eating habits. The purpose of this study is to analyze what can be done to reduce the levels of food waste at universities. To achieve this, a review of the theme’s state of the art, which is inclusive of an overview of food waste production at universities around the world, is presented. The study employed a qualitative methodology where a comprehensive review of the literature and case studies analyses from selected world regions were considered. The data indicate that a broad variance exists in producing food waste among universities, from 0.12 to 50 kg/capita/day. More factors influence the problem (e.g., gender, age, season, consumer behavior), as well as strategies to solve and prevent it (e.g., composting, recycling, new designs of packages, trayless meals, education), and benefits leading toward food waste reductions from 13 to 50%. Also, four priority actions were identified to reduce food waste at universities, and these consist of planning and awareness, food preparation and storage, services, and direct waste reuse. With appropriate adaptations, these recommended actions should be deployed as means for reducing food waste at universities around the world, while expanding learning and education in sustainability

Nonlinear Protein Degradation and the Function of Genetic Circuits

The functions of most genetic circuits require sufficient degrees of cooperativity in the circuit components. While mechanisms of cooperativity have been studied most extensively in the context of transcriptional initiation control, cooperativity from other processes involved in the operation of the circuits can also play important roles. In this study, we examine a simple kinetic source of cooperativity stemming from the nonlinear degradation of multimeric proteins. Ample experimental evidence suggests that protein subunits can degrade less rapidly when associated in multimeric complexes, an effect we refer to as cooperative stability. For dimeric transcription factors, this effect leads to a concentration-dependence in the degradation rate because monomers, which are predominant at low concentrations, will be more rapidly degraded. Thus cooperative stability can effectively widen the accessible range of protein levels in vivo. Through theoretical analysis of two exemplary genetic circuits in bacteria, we show that such an increased range is important for the robust operation of genetic circuits as well as their evolvability. Our calculations demonstrate that a few-fold difference between the degradation rate of monomers and dimers can already enhance the function of these circuits substantially. These results suggest that cooperative stability needs to be considered explicitly and characterized quantitatively in any systematic experimental or theoretical study of gene circuits.Comment: 42 pages, 10 figure