Detecting Activities of Daily Living with Smart Meters

Clement, Jana; Ploennigs, Joern; Kabitzsch, Klaus

doi:10.1007/978-3-642-37988-8_10

Cited by 16 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conversely, low active scores suggest days with reduced energy consumption, potentially indicating more periods of inactivity, which might be a sign of poor health or decreased mobility. By analyzing these scores over time, caregivers and healthcare providers can gain insights into the daily routines of elderly individuals [ 32 , 33 ] and identify any deviations from their usual patterns. Regularity assessments based on smart meter data help identify the consistency and predictability of daily routines.…”

Section: Discussionmentioning

confidence: 99%

A Lifestyle Monitoring System for Older Adults Living Independently Using Low-Resolution Smart Meter Data

Mathunjwa,

Chen,

Tsai

et al. 2024

Sensors

View full text Add to dashboard Cite

Background: Monitoring the lifestyles of older adults helps promote independent living and ensure their well-being. The common technologies for home monitoring include wearables, ambient sensors, and smart household meters. While wearables can be intrusive, ambient sensors require extra installation, and smart meters are becoming integral to smart city infrastructure. Research Gap: The previous studies primarily utilized high-resolution smart meter data by applying Non-Intrusive Appliance Load Monitoring (NIALM) techniques, leading to significant privacy concerns. Meanwhile, some Japanese power companies have successfully employed low-resolution data to monitor lifestyle patterns discreetly. Scope and Methodology: This study develops a lifestyle monitoring system for older adults using low-resolution smart meter data, mapping electricity consumption to appliance usage. The power consumption data are collected at 15-min intervals, and the background power threshold distinguishes between the active and inactive periods (0/1). The system quantifies activity through an active score and assesses daily routines by comparing these scores against the long-term norms. Key Outcomes/Contributions: The findings reveal that low-resolution data can effectively monitor lifestyle patterns without compromising privacy. The active scores and regularity assessments calculated using correlation coefficients offer a comprehensive view of residents’ daily activities and any deviations from the established patterns. This study contributes to the literature by validating the efficacy of low-resolution data in lifestyle monitoring systems and underscores the potential of smart meters in enhancing elderly people’s care.

show abstract

Section: Discussionmentioning

confidence: 99%

A Lifestyle Monitoring System for Older Adults Living Independently Using Low-Resolution Smart Meter Data

Mathunjwa,

Chen,

Tsai

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…One of the first activities is the AUTAGEF project, in which a demonstrator was developed to detect unusually long periods of inactivity in residences based on aggregated power consumption data. However, this demonstrator has so far not been evaluated in a real-life environment [ 21 , 27 , 28 ].…”

Section: Related Workmentioning

confidence: 99%

Exploiting Smart Meter Power Consumption Measurements for Human Activity Recognition (HAR) with a Motif-Detection-Based Non-Intrusive Load Monitoring (NILM) Approach

Wilhelm

Kasbauer

2021

Sensors

View full text Add to dashboard Cite

Numerous approaches exist for disaggregating power consumption data, referred to as non-intrusive load monitoring (NILM). Whereas NILM is primarily used for energy monitoring, we intend to disaggregate a household’s power consumption to detect human activity in the residence. Therefore, this paper presents a novel approach for NILM, which uses pattern recognition on the raw power waveform of the smart meter measurements to recognize individual household appliance actions. The presented NILM approach is capable of (near) real-time appliance action detection in a streaming setting, using edge computing. It is unique in our approach that we quantify the disaggregating uncertainty using continuous pattern correlation instead of binary device activity states. Further, we outline using the disaggregated appliance activity data for human activity recognition (HAR). To evaluate our approach, we use a dataset collected from actual households. We show that the developed NILM approach works, and the disaggregation quality depends on the pattern selection and the appliance type. In summary, we demonstrate that it is possible to detect human activity within the residence using a motif-detection-based NILM approach applied to smart meter measurements.

show abstract

“…Previous studies showed promise in tracking patient well being and ADLs in smart home environments based on ambient wireless sensors networks in the presence of single [13] or multiple inhabitants [15]. The application of smart meters and behavioral models have been explored in order to understand activity of elderly individuals in single apartments [10]. [2] focused on optimizing the media experiences within a multi-person household across resident categories by using an ANT-based service selection framework.…”

Section: Related Workmentioning

confidence: 99%

Smart-energy group anomaly based behavioral abnormality detection

Alam

Roy

Petruska

et al. 2016

2016 IEEE Wireless Health (WH)

View full text Add to dashboard Cite

Monitoring behavioral abnormality of individuals living independently in their own homes is a key issue for building sustainable healthcare models in smart environments. While most of the efforts have been directed towards building ambient and wearable sensors-assisted activity recognition based behavioral analysis models for remote health monitoring, energy analytics assisted behavioral abnormality prediction have rarely been investigated. In this paper, we propose a data analytic approach that helps detect energy usage anomalies corresponding to the behavioral abnormality of the residents. Our approach relies on detecting everyday appliances usage from smart meter and smart plug data traces in regular activity days and then learning the unique time segment group of each appliance's energy consumption. We focus on detecting behavioral anomalies over a set of energy source data points rather than pinpointing individual odd points. We employ hierarchical probabilistic model-based group anomaly detection [7] to interpret the anomalous behavior and therefore, detect potential tendency towards behavioral abnormality. We apply daily activity logs to evaluate our approach using two realworld energy datasets pertaining to staged functional behaviors, and show that it is possible to detect max. 97% of anomalous days with max. 87% of meaningful micro-behavioral abnormal events generating 1.1% of false alarms. However, we show that our detected abnormality can be meaningfully represented to different stakeholders such as caregivers and family members to understand the nature and severity of abnormal human behavior for sustaining better healthcare.

show abstract

Detecting Activities of Daily Living with Smart Meters

Cited by 16 publications

References 19 publications

A Lifestyle Monitoring System for Older Adults Living Independently Using Low-Resolution Smart Meter Data

A Lifestyle Monitoring System for Older Adults Living Independently Using Low-Resolution Smart Meter Data

Exploiting Smart Meter Power Consumption Measurements for Human Activity Recognition (HAR) with a Motif-Detection-Based Non-Intrusive Load Monitoring (NILM) Approach

Smart-energy group anomaly based behavioral abnormality detection

Contact Info

Product

Resources

About