Energy autonomous systems : future trends in devices, technology, and systems

The rapid evolution of electronic devices since the beginning of the nanoelectronics era has brought about exceptional computational power in an ever shrinking system footprint. This has enabled among others the wealth of nomadic battery powered wireless systems (smart phones, mp3 players, GPS, …) that society currently enjoys. Emerging integration technologies enabling even smaller volumes and the associated increased functional density may bring about a new revolution in systems targeting wearable healthcare, wellness, lifestyle and industrial monitoring applications

Effects of nonorthogonality in the time-dependent current through tunnel junctions

A theoretical technique which allows to include contributions from non-orthogonality of the electron states in the leads connected to a tunneling junction is derived. The theory is applied to a single barrier tunneling structure and a simple expression for the time-dependent tunneling current is derived showing explicit dependence of the overlap. The overlap proves to be necessary for a better quantitative description of the tunneling current, and our theory reproduces experimental results substantially better compared to standard approaches.Comment: 4 pages, 1 table, 1 figur

A miniature printed antenna with outer surface cable current suppression and low proximity effects

A miniature antenna has been designed for use in the 2.45 GHz ISM frequency band. The design, having dimensions 60 mm × 11 mm × 3.6 mm fits within an eye frame. The antenna shows an input impedance that is independent from the connected coaxial cable due to the specially shaped ground plane, including current blocking slots. The antenna performs well in free space as well as in close proximity to the human head due to an encapsulation of the antenna in a dielectric

Time efficient method for automated antenna design for wireless energy harvesting

The rectifier circuit in a rectenna (rectifying antenna) is analyzed employing a fast, efficient time-marching algorithm. The thus found complex input impedance dictates the antenna design. To maximize RF-to-DC conversion efficiency we do not want to employ an impedance matching and filtering network. Instead, we require the input impedance of the antenna to be equal to the complex conjugate value of the input impedance of the rectifier circuit. One antenna type feasible of supplying the required complex input impedance is the wire folded dipole array antenna. For this antenna type, an efficient, analytic model has been developed. The fast calculation times when implemented in software allow for an automated antenna design employing a Genetic Algorithm optimization. Thus, antenna designs can be generated within minutes employing standard office computing equipment. The design of a complete rectenna can be accomplished within hours. The direct complex conjugate matching ensures that the design is power-efficient and physically compact

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

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

Personalizing energy expenditure estimation using physiological signals normalization during activities of daily living

In this paper we propose a generic approach to reduce inter-individual variability of different physiological signals (HR, GSR and respiration) by automatically estimating normalization parameters (e.g. baseline and range). The proposed normalization procedure does not require a dedicated personal calibration during system setup. On the other hand, normalization parameters are estimated at system runtime from sedentary and low intensity activities of daily living (ADLs), such as lying and walking. When combined with activity-specific energy expenditure (EE) models, our normalization procedure improved EE estimation by 15 to 33% in a study group of 18 participants, compared to state of the art activity-specific EE models combining accelerometer and non-normalized physiological signals

Novel analytical procedures for folded strip dipole antennas

A new analysis method is described in this paper to accurately calculate the input impedance of a folded strip dipole antenna. Previous analytical procedures use a quasi-static approach to analytically predict the input impedance of an equivalent cylindrical folded dipole. The new design procedure takes advantage of new analytical equations for strip dipoles, and consequently does not need the equivalence between a rectangular and a circular cross section dipole antenna, which improves the accuracy of the analytical equations. In addition, a correction factor to the coplanar strip (CPS) transmission line length is presented to allow a wider separation between the strips of the folded dipole. Two examples are given to validate the accuracy of the newly proposed analytical procedure. The results of the analytical equations are compared with the results obtained by the electromagnetic simulation software CST Microwave Studio MWS®. The deviations between Analytical Equations (AE) and the Finite Integration Technique (FIT) do not exceed -11 dB (reflection), which makes this method 3.5 dB more accurate than the most recent reported analytical techniques (-7.5 dB). In addition, the newly proposed solution can be extended to analytically characterize different kind of antennas such as the log periodic, Yagi-Uda and folded dipole array antenna

Automatic heart rate normalization for accurate energy expenditure normalization : an analysis of activities of daily living and heart rate features

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on "Pervasive Intelligent Technologies for Health". Background: Energy Expenditure (EE) estimation algorithms using Heart Rate (HR) or a combination of accelerometer and HR data suffer from large error due to inter-person differences in the relation between HR and EE. We recently introduced a methodology to reduce inter-person differences by predicting a HR normalization parameter during low intensity Activities of Daily Living (ADLs). By using the HR normalization, EE estimation performance was improved, but conditions for performing the normalization automatically in daily life need further analysis. Sedentary lifestyle of many people in western societies urge for an in-depth analysis of the specific ADLs and HR features used to perform HR normalization, and their effects on EE estimation accuracy in participants with varying Physical Activity Levels (PALs). Objectives: To determine 1) which low intensity ADLs and HR features are necessary to accurately determine HR normalization parameters, 2) whether HR variability (HRV) during ADLs can improve accuracy of the estimation of HR normalization parameters, 3) whether HR normalization parameter estimation from different ADLs and HR features is affected by the participants’ PAL, and 4) what is the impact of different ADLs and HR features used to predict HR normalization parameters on EE estimation accuracy. Methods: We collected reference EE from indirect calorimetry, accelerometer and HR data using one single sensor placed on the chest from 36 participants while performing a wide set of activities. We derived HR normalization parameters from individual ADLs (lying, sedentary, walking at various speeds), as well as combinations of sedentary and walking activities. HR normalization parameters were used to normalized HR and estimate EE. Results: From our analysis we derive that 1) HR normalization using resting activities alone does not reduce EE estimation error in participants with different reported PALs. 2) HRV features did not show any significant improvement in RMSE. 3) HR normalization parameter estimation was found to be biased in participants with different PALs when sedentary-only data was used for the estimation. 4) EE estimation error was not reduced when normalization was carried out using sedentary activities only. However, using data from walking at low speeds improved the results significantly (30–36%). Conclusion: HR normalization parameters able to reduce EE estimation error can be accurately estimated from low intensity ADLs, such as sedentary activities and walking at low speeds (3¿– 4 km/h), regardless of reported PALs. However, sedentary activities alone, even when HRV features are used, are insufficient to estimate HR normalization parameters accurately