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

Pires, Ivan Miguel; García, Nuno M.; Flórez-Revuelta, Francisco; Spinsante, Susanna; Teixeira, Maria Cristina Canavarro

doi:10.1016/j.pmcj.2018.05.005

Cited by 41 publications

(58 citation statements)

References 42 publications

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“…This study consisted of the use of the same structure and data acquired by the research presented in [18,21,22,24,25] to implement a comparative study between three types of studies. The tests were conducted with the dataset available in [24], which included data related to the eight ADL and nine environments.…”

Section: Methodsmentioning

confidence: 99%

“…As presented in [21], an Android application was used for the acquisition of the data related to the different sensors. This mobile application is responsible for data acquisition and data processing using built-in smartphone sensors such as the accelerometer, magnetometer, gyroscope, sound, and GPS data.…”

Section: Methodsmentioning

confidence: 99%

“…This study was based on the same dataset used in [21], which is publicly available in [31]. This dataset was composed of small sets of data (five seconds every five minutes) captured by the sensors available in the off-the-shelf mobile phones, i.e., accelerometer, magnetometer, gyroscope, microphone, and GPS receiver, and stored in the cloud.…”

Section: Data Acquisitionmentioning

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: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Data Acquisitionmentioning

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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“…There are some authors that compare different sensors for measuring the physiological parameters on older people. They have found that the creation of an acceptable monitoring device for older persons requires from designers and developers to consider the special demands and abilities of the target group [12][13][14][15][16].…”

Section: Related Workmentioning

confidence: 99%

System Development for Monitoring Physiological Parameters in Living Environment

Mladenovski¹,

Achkoski²,

Goleva

2019

Lecture Notes in Computer Science

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Nowadays, sensors, algorithms and software applications are embedded in people's everyday lives. To be more precise, living environment is significantly dependent on previously mentioned technology especially in the field of medicine.The ultimate goal of this research is to collect data from different medical sensors platform, which can be used in initial medical assessment and management in the living environment. Furthermore, collected data from sport's activity were analysed in order to measure the strength of linear association between variables.First, research on market available equipment is performed which is focused on medical sensors for assisted living in people's everyday environment. Second, research on hardware in terms of tablet, desktop machines, iPad etc. is conducted and scenarios of using the hardware are elaborated. Regarding the scenarios, the concept that demonstrates the advantages and disadvantages of the system for ambient assisted living is shown. The architecture of the system followed by sensors, computer networks, terminals is included and the technology for achieving the system's functionality is presented. Next, the Zephyr Bioharness 3 sensor for measuring physiological data is used for collecting data in the living environment. Finally, integration of the sensor with the most convenient terminal in the living environment is done and collected data are visualized via MATLAB. The parameters monitored are Heart Rate (HR), Respiratory Rate (RR), Peak Acceleration (PkAcc). The proof of the correlation between listed parameters describes the physiological status in the living environment.The ratio between HR and PkAcc is 0.91. It is a strong positive correlation. It means that changes in heart beats are linearly followed by the peak acceleration. Next, the ratio between HR and RR is 0.88. It is also a strong positive correlation. It means that changes in heart beats are linearly followed by the respiratory rate. The ratio between RR and PkAcc is 0.84. Again, it is a strong positive correlation. It means that changes in respiratory rate are linearly followed by the peak acceleration.

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Towards a Deeper Understanding of the Behavioural Implications of Bidirectional Activity-Based Ambient Displays in Ambient Assisted Living Environments

Davis-Owusu

Owusu²,

Regazzoni

et al. 2019

Lecture Notes in Computer Science

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In this chapter, we investigate the extent to which the realtime bidirectional exchange of activity information can influence contextawareness, social presence, social connectedness, and importantly interpersonal activity synchrony in mediated ambient assisted living (AAL) environments. Additionally, we describe the design, development, and assessment of a bidirectional ambient display platform to support realtime activity awareness and social connectedness in mediated AAL contexts. In a semi-controlled study, we evaluate a conglomerate of activitybased lighting displays, to determine the effects of real-time bidirectional deployment on behaviour and social connectedness. Exploiting everyday objects, human activity levels are projected with a Philips Hue lamp, LED wallet, and LED walking cane, which render this information based on predefined patterns of light. Results from the current study show tendencies toward (1) an increase in implicit social interactions (e.g., the sense of experienced social presence and connectedness), (2) more positive social behaviours between the elderly and their caregivers in mediated AAL contexts, and (3) sporadic moments of interpersonal activity synchrony however, further investigation is necessary to determine the extent of this variable in mediated AAL contexts. The work conducted in this chapter was carried out while the first author was a candidate in the Joint Doctorate on Interactive and Cognitive Environments (ICE) at the Eindhoven University of Technology and the University of Genova, which resulted in the following dissertation [22].

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

Cited by 41 publications

References 42 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

System Development for Monitoring Physiological Parameters in Living Environment

Towards a Deeper Understanding of the Behavioural Implications of Bidirectional Activity-Based Ambient Displays in Ambient Assisted Living Environments

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