Recognising Activities of Daily Life Using Hierarchical Plans

The introduction of the smart home has been seen as a way of allowing elderly people to lead an independent life for longer, making sure they remain safe and in touch with their social and care communities. The assistance could be in the form of helping with everyday tasks, e.g. notifying them when the milk in the fridge will be finished or institute safeguards to mitigate risks. In order to achieve this effectively we must know what the elderly person is doing at any given time. This paper describes a tiered approach to deal with recognition of activities that addresses the problem of missing sensor events that can occur while a task is being carried out

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

Device-Free, Activity during Daily Life, Recognition Using a Low-Cost Lidar

Device-free or off-body sensing methods, such as Lidar, can be used for location-driven Activities during Daily Life (ADL) recognition without the need for a mobile host such as a human or robot to use on-body location sensors. Because if such an attachment fails, or is not operational (powered up), when such mobile hosts are device free, it still works. Hence, this paper proposes an innovative method for recognizing ADLs using a state-of-art seq2seq Recurrent Neural Network (RNN) model to classify centimeter level accurate location data from a low-cost, 360°rotating 2D Lidar device. We researched, developed, deployed and validated the system. The results indicate that it can provide a centimeter-level localization accuracy of 88% when recognizing 17 targeted location-related daily activities

Recognising Activities of Daily Life through the Usage of Everyday Objects around the Home

The integration of RFID sensors into everyday products has become a widespread solution for increasing efficiency in supply chain management. This has also led to a way of being able to monitor everyday activities in the home based on when and how these products are used, which is less intrusive than other monitoring approaches such as visual based systems. Monitoring activities in a home environment can be seen as a good way of analyzing behavior and tracking functional decline among elderly people. This paper describes a hierarchal approach for activity recognition using object usage data generated by everyday products used around the home. The motivation of this work is to allow people with early Alzheimer’s disease to have additional years of independent living before the disease reaches a stage where the person is fully dependable on someone else

Activity Recognition using a Hierarchical Framework

This paper describes an approach for modelling and detecting activities of daily life based on a hierarchy of plans that contain a range of precedence relationships, representations of concurrency and other temporal relationships. Identification of activities of daily life is achieved by episode recovery models supported by using relationships expressed in the plans. The motivation is to allow people with Alzheimer’s disease to have additional years of independent living before the Alzheimer’s disease reaches the moderate and severe stages

A Hierarchal Approach to Activity Recognition in the Home Environment based on Object Usage

Being able to monitor everyday activities of daily life is seen as a key approach for mitigating functional decline among elderly people as it allows context sensitive support to be offered. This paper describes a hierarchal approach for modelling activities of daily life using task sequences generated by object usage data and a mechanism for recognising these activities from sensor data. The underlying motivation of this work is to allow people with early Alzheimer’s disease to have additional years of independent living before the disease reaches the moderate and severe stages. To ameliorate intrusion into personal privacy the monitoring of activities is via simple non-visual sensors with a greater emphasis placed on intelligent reasoning that exploits structures of typical behaviours

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

Device-Free Daily Life (ADL) Recognition for Smart Home Healthcare using a low-cost (2D) Lidar

Device-free or off-body sensing methods such as Lidar can be used for location-related Activities during Daily Life (ADL) recognition without the need for the subject to carry less accurate on-body sensors and because some subjects may forget to carry them or maintain them to be operational (powered up), i.e., users can be device free and the method still works. Hence, this paper proposes an innovative method for recognizing daily activities using a state-of-art seq2seq Recurrent Neural Network (RNN) model to classify centimeter level accurate location data from a 360-degree rotating 2D Lidar device. We deployed and validated the system. The results indicate that our method can provide a centimeter-level localization accuracy of 88% when recognizing seventeen targeted location-related daily activities