Activities Of Daily Living Recognition Research Articles

Clustering of human activities from emerging movements

This paper is positioned in the well-established field of smart home. This area of research, highly multidisciplinary, has raised a lot of attention from researchers due to the broad real life application it could serve. One of them is the assistance of the cognitively impaired persons such as head trauma victims or persons afflicted by dementia (e.g.: Alzheimer’s disease). However, to propose powerful technological cognitive orthoses, the decades old challenge of human activity recognition must be addressed. In this paper, we propose a clustering method exploiting the Flocking algorithm for Activity of Daily Living (ADL) learning and recognition. In particular, our new method enables to both exploit events based data from the multimodal sensors of a smart home and qualitative spatial information extracted from a tracking method. Among the advantages of the method, the Flocking based algorithm does not require an initial number of clusters, unlike other clustering algorithms such as K-means. Two sets of real case scenarios were collected in our smart home laboratory, the LIARA. These sets were used to compare our method with traditional unsupervised algorithms and to evaluate the usefulness of the qualitative spatial information. The study shows that for traditional event based smart home data, the method outperforms the popular K-Means and Expectation-Maximization (EM) algorithms. Furthermore, the results indicate that not only the spatial data generalize, but it also further improve the performance regarding fine-grained ADLs recognition.

Position-Based Feature Selection for Body Sensors regarding Daily Living Activity Recognition

This paper proposes a novel approach to recognize activities based on sensor-placement feature selection. The method is designed to address a problem of multisensor fusion information of wearable sensors which are located in different positions of a human body. Precisely, the approach can extract the best feature set that characterizes each activity regarding a body-sensor location to recognize daily living activities. We firstly preprocess the raw data by utilizing a low-pass filter. After extracting various features, feature selection algorithms are applied separately on feature sets of each sensor to obtain the best feature set for each body position. Then, we investigate the correlation of the features in each set to optimize the feature set. Finally, a classifier is applied to an optimized feature set, which contains features from four body positions to classify thirteen activities. In experimental results, we obtain an overall accuracy of 99.13% by applying the proposed method to the benchmark dataset. The results show that we can reduce the computation time for the feature selection step and achieve a high accuracy rate by performing feature selection for the placement of each sensor. In addition, our proposed method can be used for a multiple-sensor configuration to classify activities of daily living. The method is also expected to deploy to an activity classification system-based big data platform since each sensor node only sends essential information characterizing itself to a cloud server.

Android Library for Recognition of Activities of Daily Living: Implementation Considerations, Challenges, and Solutions

Background:Off-the-shelf-mobile devices have several sensors available onboard that may be used for the recognition of Activities of Daily Living (ADL) and the environments where they are performed. This research is focused on the development of Ambient Assisted Living (AAL) systems, using mobile devices for the acquisition of the different types of data related to the physical and physiological conditions of the subjects and the environments. Mobile devices with the Android Operating Systems are the least expensive and exhibit the biggest market while providing a variety of models and onboard sensors.Objective:This paper describes the implementation considerations, challenges and solutions about a framework for the recognition of ADL and the environments, provided as an Android library. The framework is a function of the number of sensors available in different mobile devices and utilizes a variety of activity recognition algorithms to provide a rapid feedback to the user.Methods:The Android library includes data fusion, data processing, features engineering and classification methods. The sensors that may be used are the accelerometer, the gyroscope, the magnetometer, the Global Positioning System (GPS) receiver and the microphone. The data processing includes the application of data cleaning methods and the extraction of features, which are used with Deep Neural Networks (DNN) for the classification of ADL and environment. Throughout this work, the limitations of the mobile devices were explored and their effects have been minimized.Results:The implementation of the Android library reported an overall accuracy between 58.02% and 89.15%, depending on the number of sensors used and the number of ADL and environments recognized. Compared with the results available in the literature, the performance of the library reported a mean improvement of 2.93%, and they do not differ at the maximum found in prior work, that based on the Student’s t-test.Conclusion:This study proves that ADL like walking, going upstairs and downstairs, running, watching TV, driving, sleeping and standing activities, and the bedroom, cooking/kitchen, gym, classroom, hall, living room, bar, library and street environments may be recognized with the sensors available in off-the-shelf mobile devices. Finally, these results may act as a preliminary research for the development of a personal digital life coach with a multi-sensor mobile device commonly used daily.

Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devices

Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL). Based on pattern recognition techniques, the system developed in this study includes data acquisition, data processing, data fusion, and classification methods like Artificial Neural Networks (ANN). Multiple settings of the ANN were implemented and evaluated in which the best accuracy obtained, with Deep Neural Networks (DNN), was 89.51%. This novel approach applies L2 regularization and normalization techniques on the sensors’ data proved it suitability and reliability for the ADL recognition.

Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices.

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking into account several concepts, including data acquisition, data processing, data fusion, and pattern recognition. These concepts can be mapped onto different modules of the framework. The proposed framework should perform the identification of ADL without Internet connection, performing these tasks locally on the mobile device, taking in account the hardware and software limitations of these devices. The main purpose of this paper is to present a new approach for the creation of a framework for the recognition of ADL, analyzing the allowed sensors available in the mobile devices, and the existing methods available in the literature.

Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review.

An increase in the accuracy of identification of Activities of Daily Living (ADL) is very important for different goals of Enhanced Living Environments and for Ambient Assisted Living (AAL) tasks. This increase may be achieved through identification of the surrounding environment. Although this is usually used to identify the location, ADL recognition can be improved with the identification of the sound in that particular environment. This paper reviews audio fingerprinting techniques that can be used with the acoustic data acquired from mobile devices. A comprehensive literature search was conducted in order to identify relevant English language works aimed at the identification of the environment of ADLs using data acquired with mobile devices, published between 2002 and 2017. In total, 40 studies were analyzed and selected from 115 citations. The results highlight several audio fingerprinting techniques, including Modified discrete cosine transform (MDCT), Mel-frequency cepstrum coefficients (MFCC), Principal Component Analysis (PCA), Fast Fourier Transform (FFT), Gaussian mixture models (GMM), likelihood estimation, logarithmic moduled complex lapped transform (LMCLT), support vector machine (SVM), constant Q transform (CQT), symmetric pairwise boosting (SPB), Philips robust hash (PRH), linear discriminant analysis (LDA) and discrete cosine transform (DCT).

Fuzzy association rule mining for recognising daily activities using Kinect sensors and a single power meter

The recognition of activities of daily living (ADLs) by home monitoring systems can be helpful in order to objectively assess the health-related living behaviour and functional ability of older adults. Many ADLs involve human interactions with household electrical appliances (HEAs) such as toasters and hair dryers. Advances in sensor technology have prompted the development of intelligent algorithms to recognise ADLs via inferential information provided from the use of HEAs. The use of robust unsupervised machine learning techniques with inexpensive and retrofittable sensors is an ongoing focus in the ADL recognition research. This paper presents a novel unsupervised activity recognition method for elderly people living alone. This approach exploits a fuzzy-based association rule-mining algorithm to identify the home occupant’s interactions with HEAs using a power sensor, retrofitted at the house electricity panel, and a few Kinect sensors deployed at various locations within the home. A set of fuzzy rules is learned automatically from unlabelled sensor data to map the occupant’s locations during ADLs to the power signatures of HEAs. The fuzzy rules are then used to classify ADLs in new sensor data. Evaluations in real-world settings in this study demonstrated the potential of using Kinect sensors in conjunction with a power meter for the recognition of ADLs. This method was found to be significantly more accurate than just using power consumption data. In addition, the evaluation results confirmed that, owing to the use of fuzzy logic, the proposed method tolerates real-life variations in ADLs where the feature values in new sensor data differ slightly from those in the learning patterns.

Efficient motion estimation methods for fast recognition of activities of daily living

This work proposes a framework for the efficient recognition of activities of daily living (ADLs), captured by static color cameras, applicable in real world scenarios. Our method reduces the computational cost of ADL recognition in both compressed and uncompressed domains by introducing system level improvements in State-of-the-Art activity recognition methods. Faster motion estimation methods are employed to replace costly dense optical flow (OF) based motion estimation, through the use of fast block matching methods, as well as motion vectors, drawn directly from the compressed video domain (MPEG vectors). This results in increased computational efficiency, with minimal loss in terms of recognition accuracy. To prove the effectiveness of our approach, we provide an extensive, in-depth investigation of the trade-offs between computational cost, compression efficiency and recognition accuracy, tested on bench-mark and real-world ADL video datasets.

Engaging Mobile Data to Improve Human Well-being: the ADL Recognition Approach

The Internet of Things offers a seamlessly integrated world in which mobile devices such as smartphones play an important role. This article presents a novel, single-point service deployment system for collecting and analyzing mobile data pertaining to human users' activities of daily living (ADL), aimed at improving their well-being.

Mining intricate temporal rules for recognizing complex activities of daily living under uncertainty

Daily living activity recognition can be exploited to benefit mobile and ubiquitous computing applications. Techniques so far are mature to recognize simple actions. Due to the characteristics of diversity and uncertainty in daily living applications, most existing complex activity recognition approaches have notable limitations. First, graphical model-based approaches still lack sufficient expressive power to model rich temporal relations among activities. Second, it would be rather difficult for graphical model-based approaches to build a unified model for achieving multiple types of tasks. Third, current semantic-based approaches often fail to capture uncertainties. Fourth, formulae in these semantic-based approaches are often manually encoded. Meanwhile, it is impractical to handcraft each formula accurately in daily living scenarios where temporal relations among activities are intricate. To address these issues, we present a probabilistic semantic-based framework that combines Markov logic network with 15 temporal and hierarchical relations to explicitly perform diverse inference tasks of daily living in a unified manner. Advanced pattern mining techniques are introduced to automatically learn the propositional logic rules of intricate relations as well as their weights. Experimental results show that by logical reasoning with the mined temporal dependencies under uncertainty, the proposed model leads to an improved performance, particularly when recognizing complex activities involving the incomplete or incorrect observations of atomic actions.

Towards online and personalized daily activity recognition, habit modeling, and anomaly detection for the solitary elderly through unobtrusive sensing

Rapid population aging and advances in sensing technologies motivate the development of unobtrusive healthcare systems, designed to unobtrusively collect an elderly's personalized information of daily living and help him actively enjoy a healthy lifestyle. Existing studies towards this goal typically focus on recognition of activities of daily living (ADLs) and abnormal behavior detection. However, the applicability of these approaches is often limited by an offline analysis strategy, complex parameter tuning, obtrusive data collection, and a need for training data. To overcome these shortcomings, this paper presents a novel framework, named the online daily habit modeling and anomaly detection (ODHMAD) model, for the real-time personalized ADL recognition, habit modeling, and anomaly detection for the solitary elderly. In contrast to most existing studies which consider activity recognition and abnormal behavior detection separately, ODHMAD links both in a system. Specifically, ODHMAD performs online recognition of the elderly's daily activities and dynamically models the elderly's daily habit. In this way, ODHMAD recognizes the personalized abnormal behavior of an elderly by detecting anomalies in his learnt daily habit. The developed online activity recognition (OAR) algorithm determines the occurrence of activities by modeling the activation status of sensors. It has advantages of online learning, light parameter tuning, and no training data required. Moreover, OAR is able to obtain details of the detected activities. Experimental results demonstrate the effectiveness of the proposed OAR model for online activity recognition in terms of precision, false alarm rate, and miss detection rate.

Recognition of Activities of Daily Living with Egocentric Vision: A Review.

Video-based recognition of activities of daily living (ADLs) is being used in ambient assisted living systems in order to support the independent living of older people. However, current systems based on cameras located in the environment present a number of problems, such as occlusions and a limited field of view. Recently, wearable cameras have begun to be exploited. This paper presents a review of the state of the art of egocentric vision systems for the recognition of ADLs following a hierarchical structure: motion, action and activity levels, where each level provides higher semantic information and involves a longer time frame. The current egocentric vision literature suggests that ADLs recognition is mainly driven by the objects present in the scene, especially those associated with specific tasks. However, although object-based approaches have proven popular, object recognition remains a challenge due to the intra-class variations found in unconstrained scenarios. As a consequence, the performance of current systems is far from satisfactory.

Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments.

Activity recognition in smart environments is an evolving research problem due to the advancement and proliferation of sensing, monitoring and actuation technologies to make it possible for large scale and real deployment. While activities in smart home are interleaved, complex and volatile; the number of inhabitants in the environment is also dynamic. A key challenge in designing robust smart home activity recognition approaches is to exploit the users' spatiotemporal behavior and location, focus on the availability of multitude of devices capable of providing different dimensions of information and fulfill the underpinning needs for scaling the system beyond a single user or a home environment. In this paper, we propose a hybrid approach for recognizing complex activities of daily living (ADL), that lie in between the two extremes of intensive use of body-worn sensors and the use of ambient sensors. Our approach harnesses the power of simple ambient sensors (e.g., motion sensors) to provide additional 'hidden' context (e.g., room-level location) of an individual, and then combines this context with smartphone-based sensing of micro-level postural/locomotive states. The major novelty is our focus on multi-inhabitant environments, where we show how the use of spatiotemporal constraints along with multitude of data sources can be used to significantly improve the accuracy and computational overhead of traditional activity recognition based approaches such as coupled-hidden Markov models. Experimental results on two separate smart home datasets demonstrate that this approach improves the accuracy of complex ADL classification by over 30 %, compared to pure smartphone-based solutions.

Applying Data Mining Techniques on Continuous Sensed Data for Daily Living Activity Recognition

In this paper, several data mining techniques were discussed and analyzed in order to achieve the objective of human daily activities recognition based on a continuous sensing data set. The data mining techniques of decision tree, Naïve Bayes and Neural Network were successfully applied to the data set. The paper also proposed an idea of combining the Neural Network with the Decision Tree, the result shows that it works much better than the typical Neural Network and the typical Decision Tree model.

Skeleton-based Human Activity Recognition for Video Surveillance

Recognizing human activity is one of the important areas of computer vision research today. It plays a vital role in constructing intelligent surveillance systems. Despite the efforts in the past decades, recognizing human activities from videos is still a challenging task. Human activity may have different forms ranging from simple actions to complex activities. Recently released depth cameras provide effective estimation of 3D positions of skeletal joints in temporal sequences of depth maps. In this paper, a system for human activity recognition is proposed. We have considered the task of obtaining a descriptive labeling of the activities being performed through labeling human sub-activities. The activities we consider happen over a long period, and comprise several sub-activities performed in a sequence. The proposed activity descriptor makes the activity recognition problem viewed as a sequence classification problem. The proposed system employs Hidden Markov Models (HMMs) to recognize human activities. The system is evaluated on two benchmark datasets for daily living activity recognition. Experiment results demonstrate that the proposed system outperforms the state-of-the-art methods.

Using Temporal Logic and Model Checking in Automated Recognition of Human Activities for Ambient-Assisted Living

Automated monitoring and the recognition of activities of daily living (ADLs) is a key challenge in ambient-assisted living (AAL) for the assistance of the elderly. Within this context, a formal approach may provide a means to fill the gap between the low-level observations acquired by sensing devices and the high-level concepts that are required for the recognition of human activities. We describe a system named ARA (Automated Recognizer of ADLs) that exploits propositional temporal logic and model checking to support automated real-time recognition of ADLs within a smart environment. The logic is shown to be expressive enough for the specification of realistic patterns of ADLs in terms of basic actions detected by a sensorized environment. The online model checking engine is shown to be capable of processing a stream of detected actions in real time. The effectiveness and viability of the approach are evaluated within the context of a smart kitchen, where different types of ADLs are repeatedly performed.

An ontology-based approach to ADL recognition in smart homes

This paper presents a method for recognition of Activities of Daily Living (ADL) in smart homes. Recognition of activities of daily living and tracking them can provide unprecedented opportunities for health monitoring and assisted living applications, especially for elderly people and people with memory deficits. This paper presents Recognizing Activities of Daily Living (RADL) by discovering and monitoring patterns of ADLs in sensor equipped smart homes. The RADL is composed of two components: smart home management monitoring and ADL pattern monitoring. This paper studies the ontology base and the reasoning that are the main parts of ADL pattern monitoring. The ontology for RADL is designed and the prototype system of RADL is implemented using Protégé and Jess tools. Also, the ontology for RADL is verified by OntoCheck in automatic mode and evaluated by a metric-based approach in manual mode.

Daily living activity recognition based on statistical feature quality group selection

The benefits arising from proactive conduct and subject-specialized healthcare have driven e-health and e-monitoring into the forefront of research, in which the recognition of motion, postures and physical exercise is one of the main subjects. We propose here a multidisciplinary method for the recognition of physical activity with the emphasis on feature extraction and selection processes, which are considered to be the most critical stages in identifying the main unknown activity discriminant elements. Efficient feature selection processes are particularly necessary when dealing with huge training datasets in a multidimensional space, where conventional feature selection procedures based on wrapper methods or ‘branch and bound’ are highly expensive in computational terms. We propose an alternative filter method using a feature quality group ranking via a couple of two statistical criteria. Satisfactory results are achieved in both laboratory and semi-naturalistic activity living datasets for real problems using several classification models, thus proving that any body sensor location can be suitable to define a simple one-feature-based recognition system, with particularly remarkable accuracy and applicability in the case of the wrist.

HMM machine learning and inference for Activities of Daily Living recognition

In a human-centric smart space, Activities of Daily Living (ADL) analysis can provide very useful information for elder care and long-term care services. ADL is defined as an assessment of a person's functional status. Many recent researches concentrate on designing a good Context Aware Computing System to automate the actions necessarily triggered by ADL recognitions. Implementing a correct ADL recognition engine is a hard work, but will repay the system with lower inference errors and higher system dependability. A good ADL recognition engine is required to adjust its inference strategy based on the learning capability in order to avoid a high error rate, especially in real world inputs with a significant difference as compared to those in the training phase. In this paper, we proposed a powerful inference engine based on the Hidden Markov Model, called the Adaptive Learning Hidden Markov Model (ALHMM), which combines the Viterbi and Baum---Welch algorithms to enhance the accuracy and learning capability. The assessments of ALHMM are conducted on the Python platform and show the practical feasibility of Activity Recognition in residential homes. Such a technique can provide the key answer required for advancing the state-of-the-art in context-aware computing and applications in real life.