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This paper introduces a half-wavelength peak matching (HWPM) model, which improves the accuracy of vehicle based longitudinal road profilers used in evaluating road unevenness and mega-textures. In this application, the HWPM model is designed for profilers which utilize a laser displacement sensor with an accelerometer for detecting surface irregularities. The process of converting acceleration to displacement by double integration (which is used in most rofilers) is error-prone, and although there are techniques to minimize the effect of this error, this paper proposes a novel approach for improving the generated road profile results. The technique amends the vertical displacement derived from the accelerometer samples, by using data from the laser displacement sensor as a reference. The vehicle based profiler developed for this experiment (which uses the HWPM model) shows a huge improvement in detected longitudinal irregularities when compared with pre-processed results, and uses a 3-m rolling straight edge as a benchmark.

This paper presents the concept as well as fabrication and measurement results for a new class of MEMS accelerometers that can be operated directly by a digital processor without the need for an analog front end. Elimination of the analog front end for such digitally operated accelerometers can significantly lower the sensor power consumption. The accelerometer consists of a proof mass and a number of parallel plate electrostatic actuators that can be turned ON and OFF in a sequential manner by a digital controller. Consequently, an ON or OFF output is provided for each input condition based on an output electrode making contact with the biased proof mass. Following a simple switching algorithm, a binary search can be performed by the digital controller to find the acceleration as a binary number. Furthermore, this paper also estimates the operating power consumption for such an accelerometer. A simple 2-b version of such accelerometers has been successfully fabricated and operated in the 0-1-g range. The same device concept and the configuration can be enhanced to higher number of bits.

There is a growing interest in measuring human activities via worn inertial sensors, and situating two accelerometers on a body segment allows accessing rotational kinematic information, at a significantly lower energy cost when compared with gyroscopes. However, the placement of sensors has not been widely considered in the literature. In practice, dual-accelerometer systems should be built as compact as possible to ensure long-term wearability. In this paper, the impact of sensor placement and nature of human activity on signal quality is quantified by individual and differential signal-to-noise ratios (SNRs). To do so, noise-free signals are described by a 2-D kinematic model of a body segment as a function of kinematic variables and sensor location on the segment. Measurements are modelled as kinematic signals disturbed by zero mean additive Gaussian noise. Depending on the accuracy needed, one can choose a minimal SNR to achieve, with such dual-accelerometer arrangement. We estimate SNR and minimal sensor separations for three data sets, two from the public domain and one collected for this paper. The data sets give arm motion profiles for reaching, inertial data collected during locomotion on a treadmill and during activities of daily life. With a dual-accelerometer arrangement, we show that it is possible to achieve a good differential SNR for the analysis of various human activities if the separation between the two sensors and their placement is well chosen.

This paper presents the design, modeling, and optimization of an electromagnetic energy harvesting (EMEH) device with various tube length and winding coil width. The nonlinear magnetic-spring configuration is employed for generating sufficient power from hand shaking of irregular and low-frequency vibrations. Based on the modeling and simulation, longer tube length of the EMEH device results in lower resonant frequency and stronger nonlinearity of the system and thus is more efficient for low-frequency harvesting. From the hand shaking test, it is found that a longer tube length and a shorter winding coil width could induce a higher power generation. The optimized device of tube length of 66 mm and winding coil width of 10 mm has achieved maximum power outputs of 568.66 $\mu $ W at the hand shaking acceleration of 1 g and frequency of 6.7 Hz, which corresponds to the power density of $90.67~\mu $ W/ $\mathrm{cm}^{\mathrm {\mathbf {3}}}$ . Further improvement of the device has been achieved by increasing the coil length to 40 m. This device provides maximum power outputs of $825.36~\mu $ W at the hand shaking acceleration of 1.56 g and frequency of 6.7 Hz. This paper demonstrates a feasible design of nonlinear EMEH device with impressive output performance from hand shaking.

This paper presents theoretical and experimental results about the design of an internal feedback loop in a new capacitive micro-electro-mechanical-system velocity sensor. The sensor comprises two mass-spring inertial sensing systems connected in a series, termed the principal and secondary systems. The secondary system output is fed to an electrostatic actuator that acts between the sensor frame and the proof mass of the principal system. The aim of this internal feedback loop is to generate a sky-hook damping effect on the principal system, so that, in the frequency band of interest, the output of the sensor is proportional to the base velocity. The sensor is fabricated on a silicon-on-isolator wafer. The sensor interface and the controller are implemented on a printed circuit board. The design of the control loop is carried out offline using measured frequency response functions for the displacements of the two proof masses with respect to: 1) the base acceleration, and 2) the voltage signal driving the electrostatic actuator. This paper shows that, below 1 kHz, the measured output signal of the closed loop sensor is proportional to the velocity of the base, and above the fundamental resonance, the output signal rolls off with a phase lag of -90°.

This paper presents a sensor-based pedestrian tracking technology that does not rely on any infrastructure. The information about human walking is monitored by a sensor module composed of accelerometers, gyroscopes and magnetometers. The acquired information is used by an algorithm proposed in this paper to accurately compute the position of a pedestrian. Through the application of human kinetics, the algorithm integrates two traditional technologies: strap-down inertial navigation and pedestrian dead-reckoning. Based on the algorithm, this paper presents several methods to improve the accuracy of pedestrian tracking through reducing the integral drift which is the main cause of errors in inertial navigation. These methods have been carefully investigated through theoretical study, simulation and field experiment. The results indicate accurate tracking is achievable through the application of both the proposed algorithm and methods. Evidently, it is feasible to develop self-contained pedestrian tracking system using inertial/magnetic sensors, eliminating the need for complicated and normally expensive infrastructure that most existing tracking systems rely on.

This paper presents a multi-threshold inertial switch demonstrated experimentally and through simulation. A laterally-driven multi-threshold inertial switch based on SOIMUMPs has been fabricated. The device has been tested by a drop-table test system under different shock accelerations with various amplitudes and pulse widths. In addition to experimental data, finite element simulation has been used to further assess the device’s performance. The designed switch is a 2-axis device capable of triggering in all directions in a single plane. This work demonstrates that the designed inertial switch with 3 threshold levels can perform quantitative acceleration measurements while saving power when not activated. The multi-threshold inertial switch can sense a shock range of 800g–2600g. The same device principle can be extended for a higher number of bits. The axial disturbance performance shows that the inertial switch has high single-axial sensitivity and low axial cross-talk.

Floor Sensors (FS) are used to capture information from the force induced on the contact surface by feet during gait. On the other hand, the Ambulatory Inertial Sensors (AIS) are used to capture the velocity, acceleration and orientation of the body during different activities. In this paper, fusion of the stated modalities is performed to overcome the challenge of gait classification from wearable sensors on the lower portion of human body not in contact with ground as in FS. Deep learning models are utilized for the automatic feature extraction of the ground reaction force obtained from a set of 116 FS and body movements from AIS attached at 3 different locations of lower body, which is novel. Spatio-temporal information of disproportionate inputs obtained from the two modalities is balanced and fused within deep learning network layers whilst reserving the categorical content for each gait activity. Our approach of fusion compensates the degradation in spatio-temporal accuracies in individual modalities and makes the overall classification outcomes more accurate. Further assessment of multi-modality based results show significant improvements in f-scores using different deep learning models i.e., LSTM (99.90%), 2D-CNN (88.73%), 1D-CNN (94.97%) and ANN (89.33%) respectively.

To develop daily monitoring systems that can identify elderly people with cognitive functioning and health literacy impairments, this study examined elderly participants during the sit-to-stand (STS) movement by using a Doppler radar to gather kinematic information for assessing any associations between those data and factors related to both cognitive functioning and health literacy. More specifically, we used Doppler radar systems to measure kinematic parameters related to the STS movement among 170 community-dwelling elderly participants aged 65 years and older, who were classified into good or bad health literacy groups and cognitive-healthy or cognitive- impairment groups based on the results of conventional paper-based tests designed to assess health literacy and cognitive functioning, respectively. The Doppler radar measured kinematic parameters for not only velocity, acceleration, and jerk (the time derivative of acceleration), but also higher-order derivative parameters: snap (the time derivative of jerk) and crackle (the time derivative of snap). Intergroup differences between the measured kinematic parameters were then compared using Welch’s t-test. The results revealed that health literacy impairments were associated with velocity, acceleration, and jerk, while cognitive impairments were associated with the higher-order derivative parameters, snap and crackle. These findings show that the Doppler radar is an effective tool to distinguish impairments between health literacy and cognitive functioning based on the extraction of higher-order derivative parameters during the STS movement. The results should lead to the development of unconstrained monitoring systems that can detect early signs of impairments in both cognitive functioning and health literacy among the elderly.

Human Activity Recognition (HAR) provides the context for many user-centered personal recommender systems in areas such as healthcare, sports, lifelong learning or home automation. Based on different types of sensors (either camera based, environmental sensors or wearable and mobile sensors) user related data provides the basis to extract movement related features from which the activity that the user is performing can be assessed. Among the different types of sensors, wearable sensors provide a user convenient, non-intrusive, always available alternative that has gained special attention for HAR. Wearable sensors will be a relevant part of the Internet of Things. This paper presents a novel mechanism to detect which particular activity a user is performing based on the data from a single tri-axial accelerometer. A Convolutional Neural Network is used in order to automatically extract the most relevant features to characterize acceleration patterns with interactivity discrimination capacity. The user anchored coordinate system generating the data from the accelerometer sensor is transformed into a georeferenced coordinate system in order to estimate the horizontal and vertical acceleration components. A sliding window with 50% overlap is used to extract 5 seconds of acceleration data from which a square horizontal-vertical acceleration image is computed. Both monochrome and colored images are generated either by adding the influence of the time evolution of the acceleration series or not in the generated image. The results for both p-fold cross-validation and leave on out approaches are presented using a public dataset. The results outperform by around 8% of those obtained by the authors of the dataset in the case of using a p-fold cross-validation. This work was supported by the “ANALYTICS USING SENSOR DATA FOR FLATCITY” project TIN2016-77158-C4-1-R (MINECO/ ERDF, EU) funded by the Spanish Agencia Estatal de Investigación (AEI) and the European Regional Development Fund (ERDF).”

International audience; This paper introduces two main contributions to the wireless sensor network (WSN) society. The first one consists of modeling the relationship between the distances separating sensors and the received signal strength indicators (RSSIs) exchanged by these sensors in an indoor WSN. In this context, two models are determined using a radio-fingerprints database and kernel-based learning methods. The first one is a non-parametric regression model, while the second one is a semi-parametric regression model that combines the well-known log-distance theoretical propagation model with a non-linear fluctuation term. As for the second contribution, it consists of tracking a moving target in the network using the estimated RSSI/distance models. The target's position is estimated by combining acceleration information and the estimated distances separating the target from sensors having known positions, using either the Kalman filter or the particle filter. A fully comprehensive study of the choice of parameters of the proposed distance models and their performances is provided, as well as a study of the performance of the two proposed tracking methods. Comparisons with recently proposed methods are also provided.

Gyroscope-free inertial measurement units have been gaining popularity in applications such as motion sensing of hand-held microsurgical instruments. Various accelerometer placement configurations have been proposed in the past. However, the effect of non-identical transfer functions of accelerometers on the accuracy of angular motion sensing has been underestimated and never been taken into consideration. In this paper, significant effect of different phases at different accelerometer outputs due to non-identical transfer functions on the accuracy is highlighted, and a method is proposed to make the transfer functions of all the accelerometers identical. Effectiveness of the method is confirmed with an experiment using ADXL-203 accelerometers.