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

Pires, Ivan Miguel; García, Nuno M.; Pombo, Nuno; Flórez-Revuelta, Francisco; Spinsante, Susanna

doi:10.3390/s18020640

Cited by 26 publications

(17 citation statements)

References 118 publications

(198 reference statements)

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“…According to the previously proposed structure of a framework for the recognition of ADL and environments [2,[17][18][19][20][21][22][23][24][25], the main focus of this study was related to the data classification module, taking into account the implementations of the other modules performed in previous studies. Previously, the DNN method was implemented, and it reported reliable results.…”

Section: Discussionmentioning

confidence: 99%

“…Based on the previously proposed framework [20], Figure 1 shows a framework composed of four stages, including data acquisition, data processing, data fusion, and data classification. The data processing consisted of several phases, including data cleaning and feature extraction.…”

Section: Overview Of the Framework For The Recognition Of The Activitmentioning

confidence: 99%

“…The primary purpose of this paper is to find the best machine learning method for ADL and environment recognition. The results obtained show that IBk and AdaBoost reported better results, with complex data than the deep neural network methods.Electronics 2020, 9, 180 2 of 16The automatic recognition of ADL is widely researched [11][12][13][14][15][16], where the previously proposed framework [2,[17][18][19][20][21][22][23][24][25] was tested and validated with different types of Artificial Neural Networks (ANN) [26][27][28], verifying that the best results were achieved with Deep Neural Networks (DNN). The proposed framework allows the recognition of eight ADL, i.e., walking, running, standing, going upstairs, going downstairs, watching television, sleeping, driving, and other activities without motion, and nine environments, i.e., bar, classroom, gym, hall, kitchen, library, street, bedroom, and living room.…”

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confidence: 99%

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Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study

et al. 2020

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The recognition of Activities of Daily Living (ADL) using the sensors available in off-the-shelf mobile devices with high accuracy is significant for the development of their framework. Previously, a framework that comprehends data acquisition, data processing, data cleaning, feature extraction, data fusion, and data classification was proposed. However, the results may be improved with the implementation of other methods. Similar to the initial proposal of the framework, this paper proposes the recognition of eight ADL, e.g., walking, running, standing, going upstairs, going downstairs, driving, sleeping, and watching television, and nine environments, e.g., bar, hall, kitchen, library, street, bedroom, living room, gym, and classroom, but using the Instance Based k-nearest neighbour (IBk) and AdaBoost methods as well. The primary purpose of this paper is to find the best machine learning method for ADL and environment recognition. The results obtained show that IBk and AdaBoost reported better results, with complex data than the deep neural network methods.

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Section: Discussionmentioning

confidence: 99%

Section: Overview Of the Framework For The Recognition Of The Activitmentioning

confidence: 99%

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confidence: 99%

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Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study

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“…The HMM technique is generally utilized for the recognition of walking, strolling on stairs, standing, running, sitting and laying exercises, though the SVM, ANN and their variations, HMM and RF strategies are helpful for the recognition of complex exercises (e.g., cooking, clearing, cleaning, washing hands, medication and watering plants) [83].…”

Section: The Use Of Machine Learning For Identifying Aggressionmentioning

confidence: 99%

An Autonomous Alarm System for Personal Safety Assurance of Intimate Partner Violence Survivors Based on Passive Continuous Monitoring through Biosensors

et al. 2020

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Intimate Partner Violence (IPV) dramatically compromises the free and complete development of many women around the world, therefore leading to social asymmetry regarding the right to personal safety. In many cases, a woman who has reported her partner to police for gender-based violence needs to ensure her protection (either before the trial of the aggressor or after their freedom). Thus, it would be ideal if autonomous alarm systems could be developed in order to call the police if necessary. Up to now, many proposals have been presented in this regard, including solutions based on Information and Communication Technologies (ICT) but, unfortunately, these approaches usually rely on the active participation of the victims (survivors), who have to turn the system on by themselves if needed. Therefore, in order to overcome such limitations, in this work, a passive continuous monitoring system is proposed which uses biosensors attached to the survivor as well as machine learning techniques to infer if an abnormal situation related to gender-based violence is taking place, activating in this case an alarm. The monitoring structure of the system supervises a great deal of bio-signals according to the current status of technology of wearables and biomedical devices. The presented biosensors-based surveillance solution can also be manually disconnected for 30/60/90 min (on demand) in order to avoid false positives when a woman is, for example, practicing sports or carrying out other inoffensive activities that could incorrectly activate the alarm.

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“…Human activity recognition, detection, identification and monitoring are terms used interchangeably by various studies that implement approaches to assess the level of physical activities undertaken by individual using motion sensors [3,28,29]. In addition, the process encompasses procedures for implementation of mobile and wearable sensors based activity assessment using various sensor modalities.…”

Section: Review Of Related Workmentioning

confidence: 99%

Multi-sensor fusion based on multiple classifier systems for human activity identification

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Mujtaba

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multiview ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multisensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system.

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

Cited by 26 publications

References 118 publications

Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study

Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study

An Autonomous Alarm System for Personal Safety Assurance of Intimate Partner Violence Survivors Based on Passive Continuous Monitoring through Biosensors

Multi-sensor fusion based on multiple classifier systems for human activity identification

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