Bertrand Massot

Bio: Bertrand Massot is an academic researcher from Institut national des sciences Appliquées de Lyon. The author has contributed to research in topics: Wearable computer & Pulse wave. The author has an hindex of 11, co-authored 54 publications receiving 465 citations. Previous affiliations of Bertrand Massot include Claude Bernard University Lyon 1 & University of Lyon.

Internet of Medical Things: A Review of Recent Contributions Dealing With Cyber-Physical Systems in Medicine

Abstract: The Internet of Medical Things (IoMT) designates the interconnection of communication-enabled medical-grade devices and their integration to wider-scale health networks in order to improve patients’ health. However, because of the critical nature of health-related systems, the IoMT still faces numerous challenges, more particularly in terms of reliability, safety, and security. In this paper, we present a comprehensive literature review of recent contributions focused on improving the IoMT through the use of formal methodologies provided by the cyber-physical systems community. We describe the practical application of the democratization of medical devices for both patients and health-care providers. We also identify unexplored research directions and potential trends to solve uncharted research problems.

Partial homologies between sleep states in lizards, mammals, and birds suggest a complex evolution of sleep states in amniotes.

Abstract: It is crucial to determine whether rapid eye movement (REM) sleep and slow-wave sleep (SWS) (or non-REM sleep), identified in most mammals and birds, also exist in lizards, as they share a common ancestor with these groups. Recently, a study in the bearded dragon (P. vitticeps) reported states analogous to REM and SWS alternating in a surprisingly regular 80-s period, suggesting a common origin of the two sleep states across amniotes. We first confirmed these results in the bearded dragon with deep brain recordings and electro-oculogram (EOG) recordings. Then, to confirm a common origin and more finely characterize sleep in lizards, we developed a multiparametric approach in the tegu lizard, a species never recorded to date. We recorded EOG, electromyogram (EMG), heart rate, and local field potentials (LFPs) and included data on arousal thresholds, sleep deprivation, and pharmacological treatments with fluoxetine, a serotonin reuptake blocker that suppresses REM sleep in mammals. As in the bearded dragon, we demonstrate the existence of two sleep states in tegu lizards. However, no clear periodicity is apparent. The first sleep state (S1 sleep) showed high-amplitude isolated sharp waves, and the second sleep state (S2 sleep) displayed 15-Hz oscillations, isolated ocular movements, and a decrease in heart rate variability and muscle tone compared to S1. Fluoxetine treatment induced a significant decrease in S2 quantities and in the number of sharp waves in S1. Because S2 sleep is characterized by the presence of ocular movements and is inhibited by a serotonin reuptake inhibitor, as is REM sleep in birds and mammals, it might be analogous to this state. However, S2 displays a type of oscillation never previously reported and does not display a desynchronized electroencephalogram (EEG) as is observed in the bearded dragons, mammals, and birds. This suggests that the phenotype of sleep states and possibly their role can differ even between closely related species. Finally, our results suggest a common origin of two sleep states in amniotes. Yet, they also highlight a diversity of sleep phenotypes across lizards, demonstrating that the evolution of sleep states is more complex than previously thought.

EmoSense: An Ambulatory Device for the Assessment of ANS Activity—Application in the Objective Evaluation of Stress With the Blind

Abstract: Analysis of autonomic nervous system activity is a subject of increasing interest in the fields of health care and handicap management, as it provides information on the emotional, sensorial, and cognitive states of the patient. In this context, the simultaneous measurement of several physiological signals using small, discreet, mobile devices is required, in order to unobtrusively obtain such information under real-life conditions. We have therefore developed an ambulatory device which enables the measurement of heart rate, electrodermal activity, and skin temperature with noninvasive sensors. Wireless communication and local data storage on a memory card enables the device to be used during in-situ experiments for the analysis of autonomic nervous system activity. We have used this instrumentation in a study for the objective evaluation of stress in the blind when walking in urban space, through the analysis of electrodermal activity of blind pedestrians who independently followed a charted course involving a range of urban conditions. Experimenting in real-life settings has lead to the definition of novel, more pertinent parameters for the analysis of physiological signals in the study of autonomic nervous system activity. Results from these experiments have identified, for the first time, some rather surprising obstacles or events which give rise to an increased stress for the blind. These results were very encouraging for the use of such ambulatory devices for experiments under real- life conditions.

A wearable, low-power, health-monitoring instrumentation based on a programmable system-on-chip TM

Abstract: Improvement in quality and efficiency of health and medicine, at home and in hospital, has become of paramount importance. The solution of this problem would require the continuous monitoring of several key patient parameters, including the assessment of autonomic nervous system (ANS) activity using non-invasive sensors, providing information for emotional, sensorial, cognitive and physiological analysis of the patient. Recent advances in embedded systems, microelectronics, sensors and wireless networking enable the design of wearable systems capable of such advanced health monitoring. The subject of this article is an ambulatory system comprising a small wrist device connected to several sensors for the detection of the autonomic nervous system activity. It affords monitoring of skin resistance, skin temperature and heart activity. It is also capable of recording the data on a removable media or sending it to computer via a wireless communication. The wrist device is based on a Programmable System-on-Chip (PSoCTM) from Cypress: PSoCs are mixed-signal arrays, with dynamic, configurable digital and analogical blocks and an 8-bit Microcontroller unit (MCU) core on a single chip. In this paper we present first of all the hardware and software architecture of the device, and then results obtained from initial experiments.

Wearable sensor systems: The challenges

Abstract: Given the soaring costs associated with the treatment of ever more prevalent chronic disease, it is widely agreed that a revolution is required in health care provision. It is often thought that the necessary technology already exists for the home-based monitoring of such patients and that it is other factors which are holding back the more widespread clinical uptake of these new tools. The authors suggest that the necessary sensor-related technologies are often not as advanced as may first appear; certainly they are generally not adequate for the robust, long-term monitoring of patients under real-life conditions. An additional problem is the evident efforts to apply a given sensor and related technology platform to any and all monitoring scenarios without sufficient consideration of patient needs and the clinical requirements. The authors review the key sensing platforms and suggest the applications for which they are best suited.

Physical Human Activity Recognition Using Wearable Sensors.

Abstract: This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors' placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

Optimal Placement of Accelerometers for the Detection of Everyday Activities

Abstract: This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.