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Due to the intensive use of mobile phones for different purposes, these devices usually contain confidential information which must not be accessed by another person apart from the owner of the device. Furthermore, the new generation phones commonly incorporate an accelerometer which may be used to capture the acceleration signals produced as a result of owner's gait. Nowadays, gait identification in basis of acceleration signals is being considered as a new biometric technique which allows blocking the device when another person is carrying it. Although distance based approaches as Euclidean distance or dynamic time warping have been applied to solve this identification problem, they show difficulties when dealing with gaits at different speeds. For this reason, in this paper, a method to extract an average template from instances of the gait at different velocities is presented. This method has been tested with the gait signals of 34 subjects while walking at different motion speeds (slow, normal and fast) and it has shown to improve the performance of Euclidean distance and classical dynamic time warping.

Recently, Human Activity Recognition (HAR) has become an important research area because of its wide range of applications in several domains such as health care, elder care, sports monitoring systems, etc. The use of wearable sensors — specifically the use of inertial sensors such as accelerometers and gyroscopes — has become the most common approach to recognize physical activities because of their unobtrusiveness and ubiquity. Overall, the process of building a HAR system starts with a feature extraction phase and then a classification model is trained. In the work of Siirtola et al. is proposed an intermediate clustering step to find the homogeneous groups of activities. For the recognition step, an instance is assigned to one of the groups and the final classification is performed inside that group. In this work we evaluate the clustering-based approach for activity classification proposed by Siirtola with two additional improvements: automatic selection of the number of groups and an instance reassignment procedure. In the original work, they evaluated their method using decision trees on a sports activities dataset. For our experiments, we evaluated seven different classification models on four public activity recognition datasets. Our results with 10-fold Cross Validation showed that the method proposed by Siirtola with our additional two improvements performed better in the majority of cases as compared to using the single classification model under consideration. When using Leave One User Out Cross Validation (user independent model) we found no differences between the proposed method and the single classification model.

In this paper, we present the design and analysis of the proof mass for capacitive based MEMS accelerometers. A study was done to determine the parameters (length of hinge and number of combs) to be optimized for the MEMS accelerometer design. The proposed design can measure the acceleration in $x$-, $y$- and $z$-axes. The design features a proof mass with interdigitated fingers along each side. These interdigitated fingers act as parallel plate capacitors. Due to acceleration, capacitance changes along the comb drive. This change in capacitance can be used to monitor the acceleration. Analysis has been carried out with different comb width designs. Using the MEMS CAD tool CoventorWare, the structure has been designed, simulated and analyzed. The process flow for the fabrication has also been proposed for the above structure. Comparative study with several designs has been made and the efficient design parameters to be considered while designing MEMS accelerometer were proposed. Based on the study, a set of optimized design parameters for the comb accelerometer were reported.

Improving the operational safety of systems is essentially based on fault detection and isolation algorithms, these algorithms mainly consist in deriving the different kinds of faults while minimizing false alarms, nondetections and delays in fault detection. The choice of robust diagnosis by the bond graph approach (BG) is based on the use of a robust analytical relations redundant (ARR) generation algorithm from linear fractional transformation models (LFT), where the uncertain part of the ARR is used to generate the adaptive residue thresholds. These relationships not only allow the detection and isolation of defects on the various elements of the system, but also the location since the industrial system is modeled element by element.

The BG-LFT modeling and the robust diagnosis of an accelerometer using micro-electromechanical are presented in this paper. The interaction of the various phenomena is taken into account, thanks to the energy properties of the bond graph tool. The residuals and the adaptive thresholds for normal operation and in abnormal operation are determined. The results suggest that the use of the Bond Graph model for normal operation, the evolutions of the residuals converge towards zero, whereas a fault caused by an element (for example, the stiffness springs $k_1$ or $k_2)$ are modeled, respectively, by capacitive elements $C_1$and $C_2$ shows that the residues $r_1$, $r_2$ and $r_3$ become different from zero. These variations explain that these residues are sensitive to these elements, which are confirmed by results presented in Table 2. The analysis of the sensitivity of the residuals to parametric and structural faults is carried out to determine the detectable values of the faults and therefore to monitor the performance of the diagnosis.

In this work, a new MEMS accelerometer with large detectable range of more than 50 g is designed. Three types of flexure designs were studied: the conventional straight flexure, newly proposed interlapped-L flexure and rectangular flexure. Their dimensions were optimized to achieve the desired requirements of the accelerometer. Capacitive sensing method and electrostatic actuation are selected to be the sensing and actuation methods. Governing equations derived from this model are used to compare with that of a second order spring-mass-damper system. These mathematical models are then used to formulate the various types of sensitivities. Finite Element Analysis software ANSYS is used in the design stage to simulate its dynamic behavior. The accelerometers with interlapped-L flexure and rectangular flexure present very large detectable range of 60 g and 80 g, sensitivity of 23.1 and 17.3 fF/g, with the noise floor of 17.9 and 18.2 μg/(Hz)^1/2 in atmosphere.

In this paper, we address the problem of recognizing the semantic human activities through the analysis of large dataset collected from users’ sensor-based smartphones. Our approach is unique in terms of covering a large number of activities that users could possibly engage in, and considering the multi-level-based classification model. Our model has three properties that never seemed to be addressed by existing approaches dealing with the same problem. These are: (1) comprehensiveness — in terms of the activity set, (2) accuracy — in terms of the activity classification, and (3) applicability — in terms of flexibility in being applied in real-life settings. Current approaches do not tackle all these properties. When tested on realistic dataset, our multi-level-based model achieved promising results despite the large number of activities being considered. When compared to similar approaches, our approach achieved comparable results in terms of accuracy and outperformed them in terms of the activity types, environment and settings covered, comprehensiveness, and applicability.

The purpose of this paper is to investigate the capabilities of the inexpensive theodolite created by the authors, compared with modern commercial instruments, equipped with a Leica TPS1203 robotic total station with built-in GoPro Hero6 camera image sensors. This theodolite was tested in the laboratory where simulated dynamic displacements were determined. The results of the experimental tests showed that the displacement errors and differences between the simulated displacements of the test machine and those detected by the theodolite were in the range of $\mathrm{\Delta }=-0.15$mm to +0.13mm depending on the simulated amplitude. The theodolite was further used in the field for static and dynamic tests of the Wanzhou Railway Bridge, China. Determination of the dynamic motions of the bridge and the results of calculating the natural frequencies from the measurement data are presented. During loading tests of the bridge, the frequencies were also determined by accelerometers and these data were used as a reference to assess the accuracy of the theodolite and its suitability for dynamic tests. An original algorithm was developed in the MATLAB software environment to process the recorded videos, i.e. image processing to determine displacements and natural frequencies. It is demonstrated that the position of the theodolite at a distance of 28.5m from the bridge provides the correspondence of one pixel of 0.333mm, which provides a high level of accuracy when determining the dynamic vertical displacements of a moving target. It is established that the proposed theodolite will cost less than 5 000 euros. The results obtained are relevant for use in civil engineering for monitoring the amplitude vibrations of structures.

Tremor is the most common movement disorder and it is affecting more and more people as the world is aging. The cost involved is big considering the financial and social impact. This paper explores an assistive technology solution for upper limb pathological tremor compensation. Using both surface electromyography (SEMG) and accelerometer (ACC), a real-time pathological tremor compensation with functional electrical stimulation (FES) is proposed. One advantage of using SEMG is the electromechanical delay (SEMG data precedes the ACC data by 20–100 ms). Hence by detecting the tremor in advance, there is enough time window to do the necessary computation and to actuate the antagonist muscle by FES. This is also possible because the time taken for FES to actuate the muscle is significantly less than that of the neural signal, as detected by SEMG. For estimation of tremor parameters and separation between voluntary motion and tremor, an algorithm based on extended Kalman filter (EKF) is proposed. Experimental result from one essential tremor patient has shown 57% reduction in tremor power as measured by the ACC.

This study set out to investigate if a relationship exists between weight change and changes in 3D acceleration signals associated with walking. In addition to giving biomechanical information, this relationship could be applied in conjunction with new weight management solutions to address the excess weight problem currently plaguing the world. The study was conducted with 15 subjects. For a period of two months, they were weighed every morning and carried a 3D accelerometer during the working day. Daily accelerometric signals were recorded and signals recognized as walking were analyzed. To obtain information in a more controlled situation and higher weight change, a separate follow-up study was carried out involving one test subject performing controlled walking exercises. Our results show that a relationship does exist between weight change and 3D acceleration signals. The obtained correlation coefficient between weight change and the acceleration-related parameter was 0.21 for the combined result of all test subjects (n = 147, p = 0.01). Higher correlations were recorded for individual subjects (r = 0.97, p < 0.001). Also the follow-up with controlled walking exercises showed a high correlation (r = 0.89, p < 0.001). On the other hand, statistically significant results were not obtained for all subjects, and identical signal parameters did not always produce similar results.

Evaluation of kinetic asymmetry during walking has been important to both researchers and clinicians. Wearable devices such as accelerometers are inexpensive, easily accessible tools provide valuable information in gait analysis and offer the potential to assess asymmetry without restriction to cost-ineffective laboratory settings. The aim of this study is to investigate the feasibility of using an accelerometer in assessment of force asymmetry in gait. To this end, the relationship between asymmetry measured from force platforms and a skin-mounted accelerometer on the lower back was studied during normal walking as well as five different levels of self-induced simulated asymmetry. Results show that there is a positive overall correlation between the asymmetry indices measured by the two methods ($r=0.73$, $p<0.0001$). Future study is needed to investigate factors such as age, gender, and anthropometric properties that can help develop a predictive model.

Human activity recognition (HAR) has a wide application in daily life. With wearable sensors, people’s activity can be monitored, recorded and analysed. However, most existing methods did not make full use of human activity data and their features. In this paper, a new method based on feature selection and clustering algorithm is proposed. We established two-layer classification models, respectively, in the ankle, the chest, the wrist and the mixed accelerometer data. K-means clustering algorithm was first used to obtain a broad classification of the activities and then we conducted two rounds of classification, among which feature selection was performed in each layer. A significance analysis was also carried out in the final experiment and we compared the performance of the final model from the mixed accelerometer data with other algorithms, the results showed that the recognition performance of our model was significantly better, and the average $F$1 score was as high as 0.969 in publicly available PAMAP2 dataset. Compared with other methods, our model achieved the highest recognition rate. The method proposed in this paper can greatly improve the recognition rate of human activity and effectively evaluate daily activity.

A piezoresistive accelerometer is the first element of a vibration measurement chain, and its improvement can enhance measurement quality. In this paper, we have developed a new formula that links the movement acceleration as a function of the natural frequency and the damping rate of the piezoresistive accelerometer in first time, and movement acceleration as a function of the measurement error in second time. This model allows the decrease of the acceleration measurement error and increases the accelerometer accuracy by choosing the right damping rate and frequency range. Finally, this new formula allows proposing new parameters for more accurate and reliable piezoresistive accelerometer.

In this paper the queues are used as a method to improve maintenance performance. The information collected by vibration analysis is used to check the system status and see whether a maintenance operation is to be organized. Thus, for a precise decision, the improvement of accelerometer parameters is required. In order to solve this issue, the piezoresistive accelerometer step and impulse responses are enhanced by using appropriate parameters (damping rate and frequency range). Computer simulation tests were conducted to confirm this approach. The obtained results have shown the difference between the accelerometer with the proposed parameters and the accelerometer used in the experiment. It can be concluded that the proposed parameters provide stable and accurate accelerometer.

A new type of MEMS 3D package is introduced: stacked die Thick Quad Flat Non-lead (TQFN), for application as a multiple axis linear accelerometer. Both solder joint and die reliability during board level thermal cycling test are important concerns, as they affect the functionality and quality of the product. Design analyses are performed to study the effects of 12 key design variations in package dimensions and material properties, on solder joint reliability.

In this paper the design, fabrication and testing of the capacitive micro accelerometer with Silicon On Insulator (SOI) approach is presented. The beam location with respect to a rectangular mass is optimized, using finite element analysis (FEM) to minimize cross axis sensitivity. It is demonstrated that a simple KOH etching with the addition of the tert-butanol can be easily adopted to fabricate the accelerometer structure without any convex undercutting effects. The devices are tested by electrostatic actuation.

Noninvasive techniques, surface electromyography (sEMG) in particular, are being increasingly employed for assessing muscle activity. In these studies, local oxygen consumption and muscle metabolism are of great interest. Measurements can be performed noninvasively using optics-based methods such as near-infrared spectroscopy (NIRS). By combining energy consumption data provided by NIRS with muscle level activation data from sEMG, we may gain an insight into the metabolic and functional characteristics of muscle tissue. However, muscle motion may induce artifacts into EMG and NIRS. Thus, the inclusion of simultaneous motion measurements using accelerometers (ACMs) enhances possibilities to perceive the effects of motion on NIRS and EMG signals.

This paper reviews the current state of noninvasive EMG and NIRS-based methods used to study muscle function. In addition, we built a combined sEMG/NIRS/ACM sensor to perform simultaneous measurements for static and dynamic exercises of a biceps brachii muscle. Further, we discuss the effect of muscle motion in response of NIRS and EMG when measured noninvasively. Based on our preliminary studies, both NIRS and EMG supply specific information on muscle activation, but their signal responses also showed similarities with acceleration signals which, in this case, were supposed to be solely sensitive to motions.

This paper describes recent device developments with relaxor ferroelectric Pb(Zn_1/3Nb_2/3)O₃–PbTiO₃ (PZN–PT) single crystals carried out at Microfine Materials Technologies Pte. Ltd, Singapore. Promising [011]-poled transverse cuts of PZN–PT single crystals and the results on the effect of electric field and axial compressive stress on the rhombohedral-to-orthorhombic (R–O) phase transformation behavior of such cuts are presented and discussed. The single crystal devices described include a compact low-frequency broadband power-efficient underwater tonpilz projector, high sensitivity shear accelerometers and acoustic vector sensors (AVS). The unique characteristics offered by these PZN–PT single crystal devices are highlighted, which serve as examples of new-generation piezoelectric devices and systems for a wide range of demanding applications.

Falls are a major health concern leading cause of fatal and non-fatal injuries for neurological disorders. Balance dysfunction is one of the common factors to determine fall risk in neurological patients. Preventative measures may help to reduce the incidence and severity of falls for detecting balance function and fall risk factors. However, the objective measures for balance require expensive equipment with the assessment of clinical expertise. A main gap remains in the evaluation method to objectively characterize the balance functions in individuals with high risk of falling. With the development of wearable and mobile devices, recent advances in smart mobile devices may provide a potential opportunity to manage the gap in the detailed quantification of balance impairments. The purpose of this study is to identify whether the biomechanical data measured by the mobile device is reliable to characterize the posture stability in various balance test conditions. A total of 39 children with Down syndrome completed four balance-testing tasks under altered base of support and vision. Simultaneous biomechanical measurements were gathered from the iPod and force plate analysis system during functional balance testing. The force plate and mobile system provided similar patterns of stability across groups. Correlation ($r^2)$ between two systems for path length, 95% ellipse area, peak-to-peak, standard deviation and mean ranged from 0.60 to 0.99. We expect that the smart mobile device can provide reliable and accurate information to quantify the postural stability in individuals with elderly people or neurological disorders. The objectivity, portability and easy use of such mobile device make it ideal to apply in clinical environments for detecting balance functions and reducing the risk of falls in Down syndrome or other neurological patients.

In many situations, health care professionals need to evaluate the respiration rate (RR) for home patients. Moreover, when cases are more than health care providers’ capacity, it is important to follow up cases at home. In this paper, we present a complete system that enables healthcare providers to follow up with patients with respiratory-related diseases at home. The aim is to evaluate the use of a mobile phone’s accelerometer to capture respiration waveform from different patients using mobile phones. Whereas measurements are performed by patients themselves from home, and not by professional health care personnel, the signals captured by mobile phones are subjected to many unknowns. Therefore, the validity of the signals has to be evaluated first and before any processing. Proper signal processing algorithms can be used to prepare the captured waveform for RR computations. A validity check is considered at different stages using statistical measures and pathophysiological limitations. In this paper, a mobile application is developed to capture the accelerometer signals and send the data to a server at the health care facility. The server has a database of each patient’s signals considering patient privacy and security of information. All the validations and signal processing are performed on the server side. The patient’s condition can be followed up over a few days and an alarm system may be implemented at the server-side in case of respiration deterioration or when there is a risk of a patient’s need for hospitalization. The risk is determined based on respiration signal features extracted from the received respiration signal including RR, and Autoregressive (AR) moving average (ARMA) model parameters of the signal. Results showed that the presented method can be used at a larger scale enabling health care providers to monitor a large number of patients.

A cantilever beam and fiber Bragg grating is used to measure acceleration. The cantilever induces strain on the grating resulting in a Bragg wavelength modification that is subsequently detected. The output signal is insensitive to temperature variations and for a temperature change from −20°C to 40 °C, the output signal fluctuated less than 5% without any temperature compensation schemes. Because the sensor does not utilize expensive and complex demodulation techniques it is potentially inexpensive. For the experimental system a linear output range of 8 g could be detected.