Guillaume Lopez

Collaborative Processing of Wearable and Ambient Sensor System for Blood Pressure Monitoring

This paper describes wireless wearable and ambient sensors that cooperate to monitor a person’s vital signs such as heart rate and blood pressure during daily activities. Each wearable sensor is attached on different parts of the body. The wearable sensors require a high sampling rate and time synchronization to provide a precise analysis of the received signals. The trigger signal for synchronization is provided by the ambient sensors, which detect the user’s presence. The Bluetooth and IEEE 802.15.4 wireless technologies are used for real-time sensing and time synchronization. Thus, this wearable health-monitoring sensor response is closely related to the context in which it is being used. Experimental results indicate that the system simultaneously provides information about the user’s location and vital signs, and the synchronized wearable sensors successfully measures vital signs with a 1 ms resolution.

New healthcare society supported by wearable sensors and information mapping-based services

Collecting and analysing various personal biological and environmental sensing data is important for various medical services, for example, lifestyle self-check support, personalised medical support and medical studies for the public purpose. We propose a physiological and environmental information processing platform based on wearable sensors, for services to countermeasure to metabolic syndrome, and such lifestyle-related disease. We aim to deploy the proposed platform in five to ten years. In this paper, firstly we identify issues of daily healthcare support system. Then, we present the initiative of our research, which is composed by a unified health-related information processing platform, wearable sensors for lifestyle information collection, and flexible application architecture for development of new kind of healthcare services that offer merits to both patients and service providers.

Development of a behavior recognition system using wireless wearable information devices

Shoes for measuring foot pressure distribution pattern were developed and the algorithm of action patterns (walking, running, standing, sitting, squatting) recognition was derived. A wearable system which monitors humans movement by a wristwatch-type motion sensor and foot pressure sensing shoes that send data to a wearable PC via Bluetooth and infers context was constructed. The system gives appropriate contents on a glasses mounted display without hindering action, based on work analysis, evaluation of medical treatment, and behavior recognition, for the purpose of work support, medical treatment support, and selective dissemination information service.

Human recorder system development for sensing the autonomic nervous system

In this research, the change of the human autonomic nervous system is observed using newly developed 11g wireless electrocardiograph (ECG) telemetry. It makes possible to measure the state of the autonomic nervous system without hindering an individualpsilas movements. Experiments have been carried out in four patterns of psychological states, and ECG data processed. A specially developed data analysis program revealed that under the high stress level, the sympathetic nervous system becomes dominant. On the contrary, the parasympathetic nervous system becomes dominant with laughter and during acupunctural treatment.

Heart rate variability as a predictive biomarker of thermal comfort

Thermal comfort is an assessment of one’s satisfaction with the surroundings; yet, most mechanisms that are used to provide thermal comfort are based on approaches that preclude physiological, psychological and personal psychophysics that are precursors to thermal comfort. This leads to many people feeling either cold or hot in an environment that was supposed to be thermally comfortable to most users. To address this problem, this paper proposes to use heart rate variability (HRV) as an alternative indicator of thermal comfort status. Since HRV is linked to homeostasis, we conjectured that people’s thermal comfort could be more accurately estimated based on their heart rate variability (HRV). To test our hypothesis, we analyzed statistical, spectral, and nonlinear HRV indices of 17 human subjects doing light office work in a cold, a neutral, and a hot environment. The resulting HRV indices were used as inputs to machine learning classification algorithms. We observed that HRV is distinctively different depending on the thermal environment and that it is possible to reliably predict each subject’s thermal state (cold, neutral, and hot) with up to a 93.7% accuracy. The result of this study suggests that it could be possible to design automatic real-time thermal comfort controllers based on people’s HRV.

Continuous blood pressure measurement in daily activities

Continuous monitoring of blood pressure in daily life could improve early detection of cardiovascular disorders, as well as promoting healthcare. Conventional Ambulatory Blood Pressure Monitoring (ABPM) equipment can measure blood pressure at regular intervals for 24 hours, but is limited by long measuring time, low sampling rate, and constrained measuring posture. In this paper, we demonstrate a new method for continuous real-time measurement of blood pressure during daily activities. Our method is based on blood pressure estimation from Pulse Wave Velocity (PWV) calculation, which formula we improved to take into account changes in the inner diameter of blood vessels. Blood pressure estimation results using our new method showed a greater stability of measured data during exercise, and a better relevance than typical PWV method.

Clustering for Road Damage Locations Obtained by Smartphone Accelerometers

Crowdsensing has been widely investigated and its applications are being explored. We have used accelerometers embedded in the smartphone carried by bicycle users to detect road surface damages. However, detected damages locations are not aggregated and often, the same road surface damage can be detected several times at different locations due to measurement errors. In this study we propose a scheme to cluster damages locations to remove redundancy caused by measurement errors.

Discrimination of eating habits with a wearable bone conduction sound recorder system

Continuous monitoring of eating habits could be useful in preventing lifestyle diseases such as the metabolic syndrome. Conventional methods consist in self-reporting and mastication frequency calculation from myoelectric potential of the masseter muscle, both resulting in a significant burden for the user. We developed a non-invasive wearable sensing system that can record eating habits over a long period of time in daily life. Our original sensing system is composed by a bone conduction microphone placed in the ears, from which sound data are collected to a portable IC recorder. Applying frequency spectrum analysis on collected sound data, we could not only count the mastication number during eating, but also accurately differentiate eating, drinking, and speaking activities, which can be used to evaluate the regularity of meals. Moreover, using clustering of sound spectra, we found it is possible to classify types of foods eaten regarding their texture.

Can Sequence Mining Improve Your Morning Mood? Toward a Precise Non-invasive Smart Clock

The aim of this paper is to present our preliminary approach and work in progress in the design of sequence mining techniques for a new smart clock alarm. This clock alarm will ring the user at the most physiological opportune moment in a predefined time frame. We rely on a wearable biosensor collecting various signals (ECG, movement, temperature) and on algorithms that dynamically mine into the sequences of heterogeneous data to identify sleep cycles. The system will be less intrusive and more accurate than others. This paper presents the underlying domains, the method and the experiments we are implementing.

Development of pen based interface system for digital operation

We aim to realize a novel pen based interface system that provides various digital operations for both low-tech and high-tech people. In this paper, we propose a new digital pen device which can estimate handwriting trajectories only with embedded sensors, such as a strain gauge and a IMU (Inertia Measurement Unit). First we developed a strain gauge based digital pen device and a handwriting-trajectory-estimation algorithm specialized for it. Second we developed an IMU based digital pen device and an estimation algorithm specialize for the IMU based pen. These two pen devices and corresponding algorithms were evaluated in the estimation accuracy experimentally and we discussed an integration method of these two devices. Moreover, we developed an algorithm that associates drawn shapes, such as a circle and a rectangle, with operating commands, such as message input, address input. Using a commercially available digital pen, we confirmed that our proposed command detection and operation algorithm worked well and outperformed an usual e-mail sending operation in time.