Information and communication technology in chronic diseases: a patient’s opportunity

Scalvini, Simonetta; Baratti, Doriana; Assoni, Giuliano; Zanardini, Margherita; Comini, Laura; Bernocchi, Palmira

doi:10.1007/s12682-013-0154-1

Cited by 13 publications

(11 citation statements)

References 8 publications

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“…The data acquisition process is accomplished by a specific module in the mobile device and consists of the measurement and conversion of the electrical signals received by each sensor into a readable format [59]. Several challenges are associated with the data acquisition process when recognizing physical activities, including the positioning of the mobile device, the data sampling rate and the number of sensors to be used and hence managed [10].…”

Section: Data Acquisitionmentioning

confidence: 99%

Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare Monitoring

Jourdan

Boutet

Bahí

et al. 2020

ACM Trans. Comput. Healthcare

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The increasing popularity of wearable consumer products can play a significant role in the healthcare sector. The recognition of human activities from IoT is an important building block in this context. While the analysis of the generated datastream can have many benefits from a health point of view, it can also lead to privacy threats by exposing highly sensitive information. In this article, we propose a framework that relies on machine learning to efficiently recognise the user activity, useful for personal healthcare monitoring, while limiting the risk of users re-identification from biometric patterns characterizing each individual. To achieve that, we show that features in temporal domain are useful to discriminate user activity while features in frequency domain lead to distinguish the user identity. We then design a novel protection mechanism processing the raw signal on the user’s smartphone to select relevant features for activity recognition and normalise features sensitive to re-identification. These unlinkable features are then transferred to the application server. We extensively evaluate our framework with reference datasets: Results show an accurate activity recognition (87%) while limiting the re-identification rate (33%). This represents a slight decrease of utility (9%) against a large privacy improvement (53%) compared to state-of-the-art baselines.

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Section: Data Acquisitionmentioning

confidence: 99%

Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare Monitoring

Jourdan

Boutet

Bahí

et al. 2020

ACM Trans. Comput. Healthcare

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“…For the recognition of physical activities, the authors in [29] propose to use of inertial sensors, i.e., accelerometers and gyroscopes, complemented with orientation measurement using magnetic sensors, e.g., a compass and a magnetometer, and location measurement using location sensors, e.g., a global positioning system (GPS). The data acquisition process is accomplished by a specific module in the mobile device and consists of the measurement and conversion of the electrical signals received by each sensor into a readable format [32]. Several challenges are associated with the data acquisition process when recognizing physical activities, including the positioning of the mobile device, the data sampling rate and the number of sensors to be used and hence managed [7].…”

Section: Data Acquisitionmentioning

confidence: 99%

Toward privacy in IoT mobile devices for activity recognition

Jourdan

Boutet

Frindel

2018

Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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Recent advances in wireless sensors for personal healthcare allow to recognise human real-time activities with mobile devices. While the analysis of those datastream can have many benefits from a health point of view, it can also lead to privacy threats by exposing highly sensitive information. In this paper, we propose a privacy-preserving framework for activity recognition. This framework relies on a machine learning technique to efficiently recognise the user activity pattern, useful for personal healthcare monitoring, while limiting the risk of re-identification of users from biometric patterns that characterizes each individual. To achieve that, we first deeply analysed different features extraction schemes in both temporal and frequency domain. We show that features in temporal domain are useful to discriminate user activity while features in frequency domain lead to distinguish the user identity. On the basis of this observation, we second design a novel protection mechanism that processes the raw signal on the user's smartphone and transfers to the application server only the relevant features unlinked to the identity of users. In addition, a generalisation-based approach is also applied on features in frequency domain before to be transmitted to the server in order to limit the risk of re-identification. We extensively evaluate our framework with a reference dataset: results show an accurate activity recognition (87%) while limiting the re-identifation rate (33%). This represents a slightly decrease of utility (9%) against a large privacy improvement (53%) compared to state-of-the-art baselines.

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“…Data acquisition depends on the particular characteristics of the sensors selected to perform the recognition of ADLs, on the environments where data are captured and also on the architecture of each system. The data acquisition process is accomplished by a module embedded in the mobile device and consists of the measurement and conversion of the electrical signals received by each sensor into a readable format [ 29 ].…”

Section: Sensorsmentioning

confidence: 99%

From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

Pires

García

Flórez-Revuelta

2016

Sensors

136

105

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This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs).

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Information and communication technology in chronic diseases: a patient’s opportunity

Cited by 13 publications

References 8 publications

Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare Monitoring

Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare Monitoring

Toward privacy in IoT mobile devices for activity recognition

From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices

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