Julien Penders

Energy Harvesting for Autonomous Wireless Sensor Networks

Wireless sensor nodes (WSNs) are employed today in many different application areas, ranging from health and lifestyle to automotive, smart building, predictive maintenance (e.g., of machines and infrastructure), and active RFID tags. Currently these devices have limited lifetimes, however, since they require significant operating power. The typical power requirements of some current portable devices, including a body sensor network, are shown in Figure 1.

3D gait assessment in young and elderly subjects using foot-worn inertial sensors

This study describes the validation of a new wearable system for assessment of 3D spatial parameters of gait. The new method is based on the detection of temporal parameters, coupled to optimized fusion and de-drifted integration of inertial signals. Composed of two wirelesses inertial modules attached on feet, the system provides stride length, stride velocity, foot clearance, and turning angle parameters at each gait cycle, based on the computation of 3D foot kinematics. Accuracy and precision of the proposed system were compared to an optical motion capture system as reference. Its repeatability across measurements (test-retest reliability) was also evaluated. Measurements were performed in 10 young (mean age 26.1±2.8 years) and 10 elderly volunteers (mean age 71.6±4.6 years) who were asked to perform U-shaped and 8-shaped walking trials, and then a 6-min walking test (6MWT). A total of 974 gait cycles were used to compare gait parameters with the reference system. Mean accuracy±precision was 1.5±6.8cm for stride length, 1.4±5.6cm/s for stride velocity, 1.9±2.0cm for foot clearance, and 1.6±6.1° for turning angle. Difference in gait performance was observed between young and elderly volunteers during the 6MWT particularly in foot clearance. The proposed method allows to analyze various aspects of gait, including turns, gait initiation and termination, or inter-cycle variability. The system is lightweight, easy to wear and use, and suitable for clinical application requiring objective evaluation of gait outside of the lab environment.

Wearable, Wireless EEG Solutions in Daily Life Applications: What are we Missing?

Monitoring human brain activity has great potential in helping us understand the functioning of our brain, as well as in preventing mental disorders and cognitive decline and improve our quality of life. Noninvasive surface EEG is the dominant modality for studying brain dynamics and performance in real-life interaction of humans with their environment. To take full advantage of surface EEG recordings, EEG technology has to be advanced to a level that it can be used in daily life activities. Furthermore, users have to see it as an unobtrusive option to monitor and improve their health. To achieve this, EEG systems have to be transformed from stationary, wired, and cumbersome systems used mostly in clinical practice today, to intelligent wearable, wireless, convenient, and comfortable lifestyle solutions that provide high signal quality. Here, we discuss state-of-the-art in wireless and wearable EEG solutions and a number of aspects where such solutions require improvements when handling electrical activity of the brain. We address personal traits and sensory inputs, brain signal generation and acquisition, brain signal analysis, and feedback generation. We provide guidelines on how these aspects can be advanced further such that we can develop intelligent wearable, wireless, lifestyle EEG solutions. We recognized the following aspects as the ones that need rapid research progress: application driven design, end-user driven development, standardization and sharing of EEG data, and development of sophisticated approaches to handle EEG artifacts.

Human++: From technology to emerging health monitoring concepts

This paper gives an overview of the recent results from the Human++ research program, which targets the realization of miniaturized, intelligent and autonomous wireless sensor nodes for body area networks. It combines expertise in micro-power harvesting techniques, ultra-low-power radio, ultra-low-power DSP and sensors and actuators. This paper illustrates how technological breakthroughs in these areas lead to the emergence of new health monitoring concepts.

A low-power, wireless, 8-channel EEG monitoring headset

Micro- and nano-technology has enabled development of smaller and smarter wearable devices for medical and lifestyle related applications. In particular, recent advances in EEG monitoring technologies pave the way for wearable, wireless EEG monitoring devices. Here, a low-power wireless EEG sensor platform that measures 8-channels of EEG, is described. The platform is integrated into a wearable headset for ambulatory monitoring of EEG. While using standard EEG electrodes without conductive gel, a first evaluation shows the wireless headset is comparable to the reference system when looking at alpha wave discrimination. This device combines low-noise, and low-power functionality into an easy-to-use wireless headset, providing a first step towards a fully integrated, fully functional wearable wireless EEG monitoring system.

Estimating Energy Expenditure Using Body-Worn Accelerometers: A Comparison of Methods, Sensors Number and Positioning

Several methods to estimate energy expenditure (EE) using body-worn sensors exist; however, quantifications of the differences in estimation error are missing. In this paper, we compare three prevalent EE estimation methods and five body locations to provide a basis for selecting among methods, sensors number, and positioning. We considered 1) counts-based estimation methods, 2) activity-specific estimation methods using METs lookup, and 3) activity-specific estimation methods using accelerometer features. The latter two estimation methods utilize subsequent activity classification and EE estimation steps. Furthermore, we analyzed accelerometer sensors number and on-body positioning to derive optimal EE estimation results during various daily activities. To evaluate our approach, we implemented a study with 15 participants that wore five accelerometer sensors while performing a wide range of sedentary, household, lifestyle, and gym activities at different intensities. Indirect calorimetry was used in parallel to obtain EE reference data. Results show that activity-specific estimation methods using accelerometer features can outperform counts-based methods by 88% and activity-specific methods using METs lookup for active clusters by 23%. No differences were found between activity-specific methods using METs lookup and using accelerometer features for sedentary clusters. For activity-specific estimation methods using accelerometer features, differences in EE estimation error between the best combinations of each number of sensors (1 to 5), analyzed with repeated measures ANOVA, were not significant. Thus, we conclude that choosing the best performing single sensor does not reduce EE estimation accuracy compared to a five sensors system and can reliably be used. However, EE estimation errors can increase up to 80% if a nonoptimal sensor location is chosen.

Towards wireless emotional valence detection from EEG

Intelligent affective computers can have many medical and non-medical applications. However todays affective computers are limited in scope by their transferability to other application environments or that they monitor only one aspect of physiological emotion expression. Here, the use of a wireless EEG system, which can be implemented in a body area network, is used to investigate the potential of monitoring emotional valence in EEG, for application in real-life situations. The results show 82% accuracy for automatic classification of positive, negative and neutral valence based on film clip viewing, using features containing information on both the frequency content of the EEG and how this changes over time.

Ultra-low-power wearable biopotential sensor nodes

This paper discusses ultra-low-power wireless sensor nodes intended for wearable biopotential monitoring. Specific attention is given to mixed-signal design approaches and their impact on the overall system power dissipation. Examples of trade-offs in power dissipation between analog front-ends and digital signal processing are also given. It is shown how signal filtering can further reduce the internal power consumption of a node. Such power saving approaches are indispensable as real-life tests of custom wireless ECG patches reveal the need for artifact detection and correction. The power consumption of such additional features has to come from power savings elsewhere in the system as the overall power budget cannot increase.

Towards mental stress detection using wearable physiological sensors

Early mental stress detection can prevent many stress related health problems. This study aimed at using a wearable sensor system to measure physiological signals and detect mental stress. Three different stress conditions were presented to a healthy subject group. During the procedure, ECG, respiration, skin conductance, and EMG of the trapezius muscles were recorded. In total, 19 physiological features were calculated from these signals. After normalization of the feature values and analysis of correlations among these features, a subset of 9 features was selected for further analysis. Principal component analysis reduced these 9 features to 7 principal components (PCs). Using these PCs and different classifiers, a consistent classification accuracy between stress and non stress conditions of almost 80% was found. This suggests that a promising feature subset was found for future development of a personalized stress monitor.

Miniaturized wireless ECG-monitor for real-time detection of epileptic seizures

Recent advances in miniaturization of ultra-low power components allow for more intelligent wearable health monitors. Such systems may be used in a wide range of application areas. Here, the development and evaluation of a wireless wearable electrocardiogram (ECG) monitor to detect epileptic seizures from changes in the cardiac rhythm is described. The ECG is measured using an ultra-low-power circuit for bio-potential acquisition. The ECG data is continuously analyzed by embedded algorithms: a robust beat-detection algorithm combined with a real-time heart beat-based epileptic seizure detector. Each detected seizure candidate triggers its transmission to a receiving radio-station. At the same time, the detected events and the raw ECG data are stored on an embedded memory card from which they can be wirelessly downloaded for off-line analysis. The performance of the system in terms of power-consumption, robustness of the radio-link and comfort of use is reported. In its current implementation, the proposed ECG-monitor prototype has a size of 52x36x15mm3, and an autonomy of one day. Wireless, miniaturized and comfortable, this prototype opens new perspectives for continuous and ambulatory health monitoring.