Identifying the time profile of everyday activities in the home using smart meter data

Activities are a descriptive term for the common ways households spend their time. Examples include cooking, doing laundry, or socialising. Smart meter data can be used to generate time profiles of activities that are meaningful to households’ own lived experience. Activities are therefore a lens through which energy feedback to households can be made salient and understandable. This paper demonstrates a multi-step methodology for inferring hourly time profiles of ten household activities using smart meter data, supplemented by individual appliance plug monitors and environmental sensors. First, household interviews, video ethnography, and technology surveys are used to identify appliances and devices in the home, and their roles in specific activities. Second, ‘ontologies’ are developed to map out the relationships between activities and technologies in the home. One or more technologies may indicate the occurrence of certain activities. Third, data from smart meters, plug monitors and sensor data are collected. Smart meter data measuring aggregate electricity use are disaggregated and processed together with the plug monitor and sensor data to identify when and for how long different activities are occurring. Sensor data are particularly useful for activities that are not always associated with an energy-using device. Fourth, the ontologies are applied to the disaggregated data to make inferences on hourly time profiles of ten everyday activities. These include washing, doing laundry, watching TV (reliably inferred), and cleaning, socialising, working (inferred with uncertainties). Fifth, activity time diaries and structured interviews are used to validate both the ontologies and the inferred activity time profiles. Two case study homes are used to illustrate the methodology using data collected as part of a UK trial of smart home technologies. The methodology is demonstrated to produce reliable time profiles of a range of domestic activities that are meaningful to households. The methodology also emphasises the value of integrating coded interview and video ethnography data into both the development of the activity inference process

Monitoring meaningful activities using small low-cost devices in a smart home

A challenge associated with an ageing population is increased demand on health and social care, creating a greater need to enable persons to live independently in their own homes. Ambient assistant living technology aims to address this by monitoring occupants’ ‘activities of daily living’ using smart home sensors to alert caregivers to abnormalities in routine tasks and deteriorations in a person’s ability to care for themselves. However, there has been less focus on using sensing technology to monitor a broader scope of so-called ‘meaningful activities’, which promote a person’s emotional, creative, intellectual, and spiritual needs. In this paper, we describe the development of a toolkit comprised of off-the-shelf, affordable sensors to allow persons with dementia and Parkinson’s disease to monitor meaningful activities as well as activities of daily living in order to self-manage their life and well-being. We describe two evaluations of the toolkit, firstly a lab-based study to test the installation of the system including the acuity and placement of sensors and secondly, an in-the-wild study where subjects who were not target users of the toolkit, but who identified as technology enthusiasts evaluated the feasibility of the toolkit to monitor activities in and around real homes. Subjects from the in-the-wild study reported minimal obstructions to installation and were able to carry out and enjoy activities without obstruction from the sensors, revealing that meaningful activities may be monitored remotely using affordable, passive sensors. We propose that our toolkit may enhance assistive living systems by monitoring a wider range of activities than activities of daily living

Cities for children: the effects of car use on their lives

9-11 June 2004 In Britain, children are walking less than they used to. A major factor causing this decrease is the growth in car use. These trends are reducing children’s quantity of physical activity, with serious implications for their health. The purpose of this paper is to explore these themes using results from a 3-year research project entitled ‘Reducing children’s car use: the health and potential car dependency impacts’ which has been carried out in the Centre for Transport Studies at University College London in collaboration with others including Hertfordshire County Council, with fieldwork being carried out in Hertfordshire, an area immediately north of London. A major component of the project was a study of 200 children aged between 10 and 13 years of age using motion sensors coupled with the use of a travel and activity diary over four days. The sensors measured movement in three dimensions which was converted to activity calories, a measure of physical activity. Events from the travel and activity diaries were mapped onto the data from the sensors so that it was possible to isolate and analyse specific time periods, events and journeys. From these data, the comparative effects of different forms of transport on children’s physical activity have been established, producing clear evidence of the benefits of walking compared with car travel. It is found that the use of the car is linked to particular types of activity. For example, structured out-of-home activities, such as clubs and sports lessons tend to be reached by car while informal activities such as playing, are associated more with walking. This means that the shift from the latter to the former is one of the factors underlying children’s increasing use of the car. The motion sensors have facilitated the calculation of the intensity of various activities in terms of using activity calories. Walking is second only to physical education (PE) or games lessons in intensity. It was found that, for the older children, walking to and from school for a week used more activity calories than two hours of PE or games lessons, which is the recommended standard in Britain. It was also found that children who walk to activities are more active when they arrive at activities than those who travel by car, particularly in the more energetic activities, which suggests that walking brings wider health benefits than is generally recognised. Another strand of the project upon which this paper is based is the evaluation of walking buses. From the various surveys in the study it appears that about half of the trips on walking buses were previously walked, but there is not an equivalent decrease in the number of car trips because many of the children were being dropped at school in the course of a longer trip by a parent

Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification using Data Mining Models and Methods

This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) wearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test

Using wearable sensors to assess freezing of gait in the real world

Freezing of gait (FOG) is a debilitating symptom of Parkinson\u27s disease (PD) that remains difficult to assess. Wearable movement sensors and associated algorithms can be used to quantify FOG in laboratory settings, but the utility of such methods for real world use is unclear. We aimed to determine the suitability of our wearable sensor-based FOG assessment method for real world use by assessing its performance during in-clinic simulated real world activities. Accuracy of the sensor-based method during simulated real-world tasks was calculated using expert rated video as the gold standard. To determine feasibility for unsupervised home use, we also determined correlations between the percent of active time spent freezing (%ATSF) during unsupervised home use and in-clinic activities. Nineteen people with PD and FOG participated in this study. Results from our sensor-based method demonstrated an accuracy above 90% compared to gold-standard expert review during simulated real-world tasks. Additionally, %ATSF from our sensor-based method during unsupervised home use correlated strongly with %ATSF from our sensor-based method during in-clinic simulated real-world activities (ρ = 0.73). Accuracy values and correlation patterns suggest our method may be useful for FOG assessment in the real world

Identifying the time profile of everyday activities in the home using smart meter data

Activities are a descriptive term for the common ways households spend their time. Examples include cooking, doing laundry, or socialising. Smart meter data can be used to generate time profiles of activities that are meaningful to households’ own lived experience. Activities are therefore a lens through which energy feedback to households can be made salient and understandable. This paper demonstrates a multi-step methodology for inferring hourly time profiles of ten household activities using smart meter data, supplemented by individual appliance plug monitors and environmental sensors. First, household interviews, video ethnography, and technology surveys are used to identify appliances and devices in the home, and their roles in specific activities. Second, ‘ontologies’ are developed to map out the relationships between activities and technologies in the home. One or more technologies may indicate the occurrence of certain activities. Third, data from smart meters, plug monitors and sensor data are collected. Smart meter data measuring aggregate electricity use are disaggregated and processed together with the plug monitor and sensor data to identify when and for how long different activities are occurring. Sensor data are particularly useful for activities that are not always associated with an energy-using device. Fourth, the ontologies are applied to the disaggregated data to make inferences on hourly time profiles of ten everyday activities. These include washing, doing laundry, watching TV (reliably inferred), and cleaning, socialising, working (inferred with uncertainties). Fifth, activity time diaries and structured interviews are used to validate both the ontologies and the inferred activity time profiles. Two case study homes are used to illustrate the methodology using data collected as part of a UK trial of smart home technologies. The methodology is demonstrated to produce reliable time profiles of a range of domestic activities that are meaningful to households. The methodology also emphasises the value of integrating coded interview and video ethnography data into both the development of the activity inference process

A Case Study of a Pilot Smart Home Monitoring System with Older Adults Living Alone in East Midlands

open access articleThe aim of this project was to examine older adults and their next-of-kins’ experiences of using smart home technology. The technology unobtrusively monitors the older adult’s physical functional ability to undertake their daily activities at home. Using a case study approach, the participants comprised three family units: three older adults with a history of long-term co-morbidities and who lived alone, along with their next-of-kin (n=4). The older adults were all female aged between 72- 82 years of age, while the four next-of-kin were all females aged between 40 and 55 years of age. Participants experiences of using the smart home technology was evaluated at three- and eight-months post installation via in-depth one-to-one interviews with the older adults and their next-of-kin. The older adults described how the smart home sensors reduced their levels of anxiety because they were ‘not feeling alone’. Likewise, their next-of-kin described how the sensors gave them an insight to their older relatives’ activities of daily living, as well as the challenges they experienced. The findings highlighted the benefit of smart home technologies in terms of helping older adults and their next-of-kin monitor their daily activities, reduce social isolation, and adopt positive health and behavioural changes

Continuous Monitoring of Vital Signs With Wearable Sensors During Daily Life Activities:Validation Study

BACKGROUND: Continuous telemonitoring of vital signs in a clinical or home setting may lead to improved knowledge of patients’ baseline vital signs and earlier detection of patient deterioration, and it may also facilitate the migration of care toward home. Little is known about the performance of available wearable sensors, especially during daily life activities, although accurate technology is critical for clinical decision-making. OBJECTIVE: The aim of this study is to assess the data availability, accuracy, and concurrent validity of vital sign data measured with wearable sensors in volunteers during various daily life activities in a simulated free-living environment. METHODS: Volunteers were equipped with 4 wearable sensors (Everion placed on the left and right arms, VitalPatch, and Fitbit Charge 3) and 2 reference devices (Oxycon Mobile and iButton) to obtain continuous measurements of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO(2)), and temperature. Participants performed standardized activities, including resting, walking, metronome breathing, chores, stationary cycling, and recovery afterward. Data availability was measured as the percentage of missing data. Accuracy was evaluated by the median absolute percentage error (MAPE) and concurrent validity using the Bland-Altman plot with mean difference and 95% limits of agreement (LoA). RESULTS: A total of 20 volunteers (median age 64 years, range 20-74 years) were included. Data availability was high for all vital signs measured by VitalPatch and for HR and temperature measured by Everion. Data availability for HR was the lowest for Fitbit (4807/13,680, 35.14% missing data points). For SpO(2) measured by Everion, median percentages of missing data of up to 100% were noted. The overall accuracy of HR was high for all wearable sensors, except during walking. For RR, an overall MAPE of 8.6% was noted for VitalPatch and that of 18.9% for Everion, with a higher MAPE noted during physical activity (up to 27.1%) for both sensors. The accuracy of temperature was high for VitalPatch (MAPE up to 1.7%), and it decreased for Everion (MAPE from 6.3% to 9%). Bland-Altman analyses showed small mean differences of VitalPatch for HR (0.1 beats/min [bpm]), RR (−0.1 breaths/min), and temperature (0.5 °C). Everion and Fitbit underestimated HR up to 5.3 (LoA of −39.0 to 28.3) bpm and 11.4 (LoA of −53.8 to 30.9) bpm, respectively. Everion had a small mean difference with large LoA (−10.8 to 10.4 breaths/min) for RR, underestimated SpO(2) (>1%), and overestimated temperature up to 2.9 °C. CONCLUSIONS: Data availability, accuracy, and concurrent validity of the studied wearable sensors varied and differed according to activity. In this study, the accuracy of all sensors decreased with physical activity. Of the tested sensors, VitalPatch was found to be the most accurate and valid for vital signs monitoring

Protocol for PD SENSORS:Parkinson’s Disease Symptom Evaluation in a Naturalistic Setting producing Outcomes measuRes using SPHERE technology. An observational feasibility study of multi-modal multi-sensor technology to measure symptoms and activities of daily living in Parkinson’s disease

Introduction The impact of disease-modifying agents on disease progression in Parkinson’s disease is largely assessed in clinical trials using clinical rating scales. These scales have drawbacks in terms of their ability to capture the fluctuating nature of symptoms while living in a naturalistic environment. The SPHERE (Sensor Platform for HEalthcare in a Residential Environment) project has designed a multi-sensor platform with multimodal devices designed to allow continuous, relatively inexpensive, unobtrusive sensing of motor, non-motor and activities of daily living metrics in a home or a home-like environment. The aim of this study is to evaluate how the SPHERE technology can measure aspects of Parkinson’s disease.Methods and analysis This is a small-scale feasibility and acceptability study during which 12 pairs of participants (comprising a person with Parkinson’s and a healthy control participant) will stay and live freely for 5 days in a home-like environment embedded with SPHERE technology including environmental, appliance monitoring, wrist-worn accelerometry and camera sensors. These data will be collected alongside clinical rating scales, participant diary entries and expert clinician annotations of colour video images. Machine learning will be used to look for a signal to discriminate between Parkinson’s disease and control, and between Parkinson’s disease symptoms ‘on’ and ‘off’ medications. Additional outcome measures including bradykinesia, activity level, sleep parameters and some activities of daily living will be explored. Acceptability of the technology will be evaluated qualitatively using semi-structured interviews.Ethics and dissemination Ethical approval has been given to commence this study; the results will be disseminated as widely as appropriate

Activity Recognition using Hierarchical Hidden Markov Models on Streaming Sensor Data

Activity recognition from sensor data deals with various challenges, such as overlapping activities, activity labeling, and activity detection. Although each challenge in the field of recognition has great importance, the most important one refers to online activity recognition. The present study tries to use online hierarchical hidden Markov model to detect an activity on the stream of sensor data which can predict the activity in the environment with any sensor event. The activity recognition samples were labeled by the statistical features such as the duration of activity. The results of our proposed method test on two different datasets of smart homes in the real world showed that one dataset has improved 4% and reached (59%) while the results reached 64.6% for the other data by using the best methods