An Event-Based Approach for Discovering Activities of Daily Living by Hidden Markov Models

Viard, Kévin; Fanti, Maria Pia; Faraut, Grégory; Lesage, Jean-Jacques

doi:10.1109/iucc-css.2016.020

Cited by 15 publications

(21 citation statements)

References 15 publications

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“…In [27] the authors focus on the activity discovering problem, proposing an approach to build a model under the form of Hidden Markov Model (HMM), from a training database of observed events emitted by binary sensors, without the knowledge of actions really performed during the learning period. The discovering problem is presented also in [28,29], where a Discontinuous Varied-Order Sequential Miner (DVSM) algorithm is used to discover frequent activities that are continuously recorded in a smart environment and combined with a clustering algorithm to find frequent occurrences of activities and cluster familiar patterns together.…”

Section: Activity Recognition In Smart Buildingsmentioning

confidence: 99%

Sensor-Based Early Activity Recognition Inside Buildings to Support Energy and Comfort Management Systems

2019

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Building Energy and Comfort Management (BECM) systems have the potential to considerably reduce costs related to energy consumption and improve the efficiency of resource exploitation, by implementing strategies for resource management and control and policies for Demand-Side Management (DSM). One of the main requirements for such systems is to be able to adapt their management decisions to the users’ specific habits and preferences, even when they change over time. This feature is fundamental to prevent users’ disaffection and the gradual abandonment of the system. In this paper, a sensor-based system for analysis of user habits and early detection and prediction of user activities is presented. To improve the resulting accuracy, the system incorporates statistics related to other relevant external conditions that have been observed to be correlated (e.g., time of the day). Performance evaluation on a real use case proves that the proposed system enables early recognition of activities after only 10 sensor events with an accuracy of 81 % . Furthermore, the correlation between activities can be used to predict the next activity with an accuracy of about 60 % .

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Section: Activity Recognition In Smart Buildingsmentioning

confidence: 99%

Sensor-Based Early Activity Recognition Inside Buildings to Support Energy and Comfort Management Systems

2019

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“…In this work, it is assumed that the set of activities to recognise is fixed by a medical staff. At the end of the AD step, each activity is modelled by a probabilistic finite-state automaton obtained by using the discovery method described in [9].…”

Section: A Problem Unformal Definitionmentioning

confidence: 99%

“…This activity has been performed several times in a smart flat and the corresponding observed sequences of events have been stored in a database. From this database, a model of this activity has been first built by applying the AD method presented in [9]. Afterwards, the performance of normalised likelihood for ADL on-line recognition has been studied.…”

Section: A Experimental Protocolmentioning

confidence: 99%

“…By applying the AD method presented in [9] to the learning database, the PFA presented Fig. 6 is automatically obtained.…”

Section: Fig 5 Preparing Hot Beverage (Phb) Activity Decompositionmentioning

confidence: 99%

“…The presented methods require a perfect knowledge of activities actually performed during the learning phase, which is information difficult to obtain with the required completeness and accuracy [8]. In a previous work [9], we proposed an approach of discovering ADLs models by HMMs, but which not requires the knowledge of the activities actually performed during the learning period. In this paper, the same approach is used for modelling ADLs, but under the form of standard Probabilistic Finite-State Automata (PFA).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recognition of human activity based on probabilistic finite-state automata

Viard

Fanti

Faraut

et al. 2017

2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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Smart home technologies are a promising way to improve health safety of frail people living alone at home. They allow for example on-line recognition of Activities of Daily Living (ADLs) performed by a person, in order to detect dangerous or unusual behaviour. Since human behaviour is not deterministic, probabilistic approaches are often used for ADL recognition, despite difficulties encountered in model building and probabilistic indicators computing. In this paper, it is proposed an approach, based on a Probabilistic Finite State Automata, to detect which activity is being performed. For that a new indicator, called the normalised likelihood, is proposed. The robustness of this indicator to the size of the observed behaviour as well as its computational complexity are also addressed. Finally, the quality of the obtained results are discussed on the basis of an experiment performed in a living lab.

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Long-term deviation detection in human behavior

Fouquet

Faraut

Lesage

2021

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

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World population ageing causes an important increase of people needing specific health care and monitoring. Dedicated institutions exist, but most of the elderly prefer to keep their autonomy for economic and personal reasons. To ensure a good quality of life and health to this population, Health at Home (HaH) solutions are explored. Many works focus on monitoring smart home inhabitant behavior to detect changes which might be due to health problems. These approaches are efficient to detect accident or short-term diseases such as a cold or influenza but tend to detect too tardily diseases which provoke slow declines in behavior. This is a problem as the elderly are likely to suffer from such troubles and early detection allows for better diagnosis and may help to prevent or reduce future worsening. In this paper, a novel approach for the detection of long-term behavior changes is introduced. It focuses on activity duration as this indicator is influenced by most diseases and give clear information about the inhabitant health status. This paper proposes data forecasting to detect future anomalies to assess existence of evolution in the current behavior. Information is sent to medical staff to refine their prognostic and adapt their treatment or call for a medical appointment. A case study based on a real smart home simulating a worst-case scenario attests for the efficiency of the approach and its resilience.

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An Event-Based Approach for Discovering Activities of Daily Living by Hidden Markov Models

Cited by 15 publications

References 15 publications

Sensor-Based Early Activity Recognition Inside Buildings to Support Energy and Comfort Management Systems

Sensor-Based Early Activity Recognition Inside Buildings to Support Energy and Comfort Management Systems

Recognition of human activity based on probabilistic finite-state automata

Long-term deviation detection in human behavior

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