Contactless detection is one of the new frontiers of technological innovation in the field of healthcare, enabling unobtrusive measurements of biomedical parameters. Compared to conventional methods for Heart Rate (HR) detection that employ expensive and/or uncomfortable devices, such as the Electrocardiograph (ECG) or pulse oximeter, contactless HR detection offers fast and continuous monitoring of heart activities and provides support for clinical analysis without the need for the user to wear a device. This paper presents a validation study for a contactless HR estimation method exploiting RGB (Red, Green, Blue) data from a Microsoft Kinect v2 device. This method, based on Eulerian Video Magnification (EVM), Photoplethysmography (PPG) and Videoplethysmography (VPG), can achieve performance comparable to classical approaches exploiting wearable systems, under specific test conditions. The output given by a Holter, which represents the gold-standard device used in the test for ECG extraction, is considered as the ground-truth, while a comparison with a commercial smartwatch is also included. The validation process is conducted with two modalities that differ for the availability of a priori knowledge about the subjects’ normal HR. The two test modalities provide different results. In particular, the HR estimation differs from the ground-truth by 2% when the knowledge about the subject’s lifestyle and his/her HR is considered and by 3.4% if no information about the person is taken into account.
Gambi, E.; Agostinelli, A.; Belli, A.; Burattini, L.; Cippitelli, E.; Fioretti, S.; Pierleoni, P.; Ricciuti, M.; Sbrollini, A.; Spinsante, S. Heart Rate Detection Using Microsoft Kinect: Validation and Comparison to Wearable Devices. Sensors 2017, 17, 1776.
The proposed dataset provides a complete set of simultaneously acquired data from contactless and wearable devices for direct and indirect heart-rate measurement. Data were acquired on a total of 20 healthy white Caucasian subjects wearing no makeup (10 males and 10 females; age: 22.50 ± 1.57 years; height: 173 ± 10 cm; weight: 62.80 ± 9.52 kg) and consisted of: i) videos of the subject's face acquired by a RGB-D (Red, Green, Blue and Depth) camera (Microsoft Kinect v2), which is a contactless device; ii) electrocardiographic (ECG) recordings acquired by a clinical Holter ECG recorder (Global Instrumentation's M12R Holter), which is a wearable device; and iii) heart-rate measurements acquired from a commercial smartwatch (Moto 360 smartwatch by Motorola), which is also a wearable device. ECG recordings were processed to extract the R-peaks position and obtain a reference indirect measurement of the heart rate. A direct measurement of the heart rate was provided by the commercial smartwatch. The dataset here presented could be useful to develop new algorithms for heart-rate detection from contactless devices and to validate contactless heart-rate estimation in comparison to reference heart rate from clinical wearable devices and to heart rate from commercial wearable devices.
Paola Pierleoni, Ennio Gambi, Manola Ricciuti, Agnese Sbrollini, Lorenzo Palma, Alberto Belli, Micaela Morettini, Laura Burattini, "Simultaneously acquired data from contactless and wearable devices for direct and indirect heart-rate measurement", Data in Brief, Volume 26, 2019
Technologies for contactless Heart Rate measure- ment support the progress in the diagnostic and healthcare fields, opening new possibilities even for everyday use at home. Among them, Videoplethysmography based on the Eulerian Video Mag- nification method has been already validated as an effective alternative to traditional, but often bulky, Electrocardiographic acquisitions. In this paper we study the influence of different measurement parameters on the Heart Rate estimation, in order to assess the reliability of the Videoplethysmography detection method under varying conditions, like different dimensions and positions of the processed regions of interest, pyramidal decomposition levels, and light conditions.
A. Del Campo, E. Gambi, L. Montanini, D. Perla, L. Raffaeli and S. Spinsante, "MQTT in AAL systems for home monitoring of people with dementia," 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Valencia, 2016, pp. 1-6.
The adoption of the Internet of Things paradigm in Ambient Assisted Living platforms requires the investigation and analysis of issues related to data collection and processing. In fact, the peculiarities of Ambient Assisted Living services and applications pose specific requirements on the way data originated from sensors should be processed (locally or remotely), delivered (as raw data, or in aggregated fashion), and, of course, how they should be shared or protected. This paper analyses the issues related to data management starting from a review of the state of the art, in order to draw general trends or widespread approaches, that are then discussed and evaluated with respect to a practical implementation presented as a case study.
Gambi E., Ricciuti M., Spinsanye S., Sensitivity of the Contactless Videoplethysmography-Based Heart Rate Detection to Different Measurement Conditions, Proc. of 2018 26th European Signal Processing Conference (EUSIPCO)