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The piezoelectric coupling principle is widely used (along with capacitive coupling and piezoresistive coupling) for MEMS accelerometers. Piezoelectric MEMS accelerometers are used primarily for vibration monitoring. Polymer piezoelectric MEMS accelerometers offer the merits of heavy-metal-free structure material and simple microfabrication flow. More importantly, polymeric piezoelectric MEMS accelerometers may be the basis of novel applications, such as fully organic inertial sensing microsystems using polymer sensors and organic integrated circuits. This paper presents a novel polymer piezoelectric MEMS accelerometer design using PVDF films. A simple and rapid microfabrication flow based on laser micromachining of thin films and 3D stereolithography was developed to fabricate three samples of this design. During proof-of-concept experiments, the design achieved a sensitivity of 21.82 pC/g (equivalent open-circuit voltage sensitivity: 126.32 mV/g), a 5% flat band of 58.5 Hz, and a noise density of 6.02 µg/√Hz. Thus, this design rivals state-of-the-art PZT-based counterparts in charge sensitivity and noise density, and it surpasses the performance capabilities of several commercial MEMS accelerometers. Moreover, this design has a 10-times smaller device area and a 4-times larger flat band than previous state-of-the-art organic piezoelectric MEMS accelerometers. These experimentally validated performance metrics demonstrate the promising application potential of the polymeric piezoelectric MEMS accelerometer design presented in this article. [ABSTRACT FROM AUTHOR]

The study of the vibration phenomenon of pebbles under turbulence is still a gap despite recent technological advancements in measurement capabilities. In this study, the vibration process of a fully exposed, isolated smart pebble on a rough bed was measured using a miniature inertial accelerometer and combined with simultaneous local measurements of the near-bed velocities. The paper conducts a series of experimental studies with different flow conditions. The test data match well with the manually observed phenomena, indicating the authenticity of the sediment vibration data collected by the measurement system. The test results show that the pebble motion (before entrainment) subjected to turbulence is a nonlinear vibration process, and its vibration types include in-situ vibration and ex-situ strong vibration. The probability distribution for the amplitude of vibration acceleration is well parameterized by the normal distribution. The vibration intensity tends to increase before approaching the threshold, but it weakens when approaching the point. The sediment vibration frequency is within 20–25 Hz, similar to the flow pulsation frequency and belongs to low-frequency vibration. The data indicate that the near-bed flow velocity is most directly related to the particle vibration events. [ABSTRACT FROM AUTHOR]

This paper analyzes the influence of three different shape memory alloy (SMA) wires (i.e., Ni-Ti, Cu-Al-Ni, and Cu-Zn wires) for mitigating model cable vibration. Shape memory alloy (SMA) wires because of their superelasticity or shape memory properties can promise to be one of the effective damping materials in suppressing stay cable oscillations. The investigation explores through experimental works, the capabilities of three Ni-Ti, Cu-Al-Ni, and Cu-Zn SMA wires by estimating the vibration amplitude suppression of the model stay cable. The effective location of the SMA wires on the model cable for vibration suppression was estimated in free as well as forced vibration cases. To impart free and forced vibration, respectively, in the cable, a transducer-based hammer and exciter were used. An accelerometer and data acquisition system was used to record and acquire vibration data. It is also very appropriate to ascertain which wire among one of the three SMA wires performs well. It is found that the Ni-Ti SMA wire has a better performance in vibration suppression as compared to Cu-Al-Ni and Cu-Zn wires. It is also established that the attachment of the SMA wire at the mid-length of the cable gives better vibration suppression for the first mode. [ABSTRACT FROM AUTHOR]

Smartphones and wearables are widely recognised as the foundation for novel Digital Health Technologies (DHTs) for the clinical assessment of Parkinson's disease. Yet, only limited progress has been made towards their regulatory acceptability as effective drug development tools. A key barrier in achieving this goal relates to the influence of a wide range of sources of variability (SoVs) introduced by measurement processes incorporating DHTs, on their ability to detect relevant changes to PD. This paper introduces a conceptual framework to assist clinical research teams investigating a specific Concept of Interest within a particular Context of Use, to identify, characterise, and when possible, mitigate the influence of SoVs. We illustrate how this conceptual framework can be applied in practice through specific examples, including two data-driven case studies. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic analysis of a tunable novel triple-electrode variable capacitor is presented. It is subjected to a base excitation that can act as an accelerometer with a wide measurement range. A clamped–clamped porous microbeam connected to a frame and a proof mass in the middle of it form the structure. The sensing section of the accelerometer is in the form of capacitive sensing combs in which the fixed fingers connected to the frame are subjected to a biasing DC voltage ( V S ). In the tuning section of the accelerometer, two fixed fingers (electrodes) connected to the frame are subjected to a DC tuning voltage ( V T ), and two moving free electrical charge fingers connected to the beam act as a sliding connector. A DC tuning voltage applied to the fixed electrodes generates an electrostatic force that works as a tunable voltage-sliding nonlinear spring in the structure. Coupled nonlinear differential equations of the transversal and longitudinal vibration of the structure in the presence of the base excitation are extracted. The steady-state solution of the accelerometer is obtained based on a physically based learning method that makes it possible to obtain its frequency response for the first harmony as well as for the higher harmonies and to predict primary and secondary resonances in different harmonies of the response. The transient response of the accelerometer in exposure to various types of external acceleration fields such as step, single pulse, and repetitive pulse is investigated. The frequency response of the accelerometer for different values of tuning voltage is studied. It is shown that the tuning stiffness of this voltage-sliding spring provides a highly effective solution to realize instrumentation with wide-range measurement. The presented tunability can enable the structure to act as an effective accelerometer, microresonator, energy harvester, and so on. [ABSTRACT FROM AUTHOR]

Nocturnal scratch is one major factor leading to impaired quality of life in atopic dermatitis (AD) patients. Therefore, objectively quantifying nocturnal scratch events aids in assessing the disease state, treatment effect, and AD patients' quality of life. In this paper, we describe the use of actigraphy, highly predictive topological features, and a model-ensembling approach to develop an assessment of nocturnal scratch events by measuring scratch duration and intensity. Our assessment is tested in a clinical setting against the ground truth obtained from video recordings. The new approach addresses unmet challenges in existing studies, such as the lack of generalizability to real-world applications, the failure to capture finger scratches, and the limitations in the evaluation due to imbalanced data in the current literature. Furthermore, the performance evaluation shows agreement between derived digital endpoints and the video annotation ground truth, as well as patient-reported outcomes, which demonstrated the validity of the new assessment of nocturnal scratch. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has gained increasing popularity in a large variety of mission-critical fields, such as aerospace, medical, and transportation. The layer-by-layer fabrication scheme of the AM significantly enhances fabrication flexibility, resulting in the expanded vulnerability space of cyber-physical AM systems. This potentially leads to altered AM parts with compromised mechanical properties and functionalities. Furthermore, those internal alterations in the AM builds are very challenging to detect using the traditional geometric dimensioning and tolerancing (GD&T) features. Therefore, how to effectively monitor and accurately detect cyber-physical attacks becomes a critical barrier for the broader adoption of AM technology. To address this issue, this paper proposes a machine learning-driven online side channel monitoring approach for AM process authentication. A data-driven feature extraction approach based on the LSTM-autoencoder is developed to detect the unintended process/product alterations caused by cyber-physical attacks. Both supervised and unsupervised monitoring schemes are implemented based on the extracted features. To validate the effectiveness of the proposed method, real-world case studies were conducted using a fused filament fabrication (FFF) platform equipped with two accelerometers. In the case study, two different types of cyber-physical attacks are implemented to mimic the potential real-world process alterations. Experimental results demonstrate that the proposed method outperforms conventional process monitoring methods, and it can effectively detect part geometry and layer thickness alterations in a real-time manner. [ABSTRACT FROM AUTHOR]

With the increasing demand for multidirectional vibration measurements, traditional triaxial accelerometers cannot achieve vibration measurements with high sensitivity, high natural frequency, and low cross-sensitivity simultaneously. Moreover, for piezoresistive accelerometers, achieving pure axial deformation of the piezoresistive beam can greatly improve performance, but it requires the piezoresistive beam to be located in a specific position, which inevitably makes the design more complex and limits the performance improvement. Here, a monolithically integrated triaxial high-performance accelerometer with pure axial stress piezoresistive beams was designed, fabricated, and tested. By controlling synchronous displacements at both piezoresistive beam ends, the pure axial stress states of the piezoresistive beams could be easily achieved with position independence without tedious calculations. The measurement unit for the z-axis acceleration was innovatively designed as an interlocking proof mass structure to ensure a full Wheatstone bridge for sensitivity improvement. The pure axial stress state of the piezoresistive beams and low cross-sensitivity of all three units were verified by the finite element method (FEM). The triaxial accelerometer was fabricated and tested. Results showing extremely high sensitivities (x axis: 2.43 mV/g/5 V; y axis: 2.44 mv/g/5 V; z axis: 2.41 mV/g/5 V (without amplification by signal conditioning circuit)) and high natural frequencies (x/y axes: 11.4 kHz; z-axis: 13.2 kHz) were obtained. The approach of this paper makes it simple to design and obtain high-performance piezoresistive accelerometers. [ABSTRACT FROM AUTHOR]

Researchers are paying increasing attention to the development of low-cost and microcontroller-based accelerometers, in order to make structural health monitoring feasible for conventional bridges with limited monitoring budget. Parallel with the low-cost sensor development, the use of the embedded accelerometers of smartphones for eigenfrequency analysis of bridges is becoming popular in the civil engineering literature. This paper, for the first time in the literature, studies these two promising technologies by comparing the noise density and eigenfrequency analysis of a self-developed, validated and calibrated low-cost Internet of things based accelerometer LARA (low cost adaptable reliable accelerometer) with those of a state of the art smartphone (iPhone XR). The eigenfrequency analysis of a footbridge in San Sebastian, Spain, showed that the embedded accelerometer of the iPhone XR can measure the natural frequencies of the under study bridge. [ABSTRACT FROM AUTHOR]

This paper focuses on the effect of temperature on the frequency response function (FRF) of three different structural adhesives; namely a two component methylmethacrylate (HBM X60), a modified silane (Terostat 939) and a cyanoacrylate (Loctite 454). The structural adhesives are commonly used in vibration analysis to mount accelerometers on structures or machines. The stiffness of the adhesive can influence the response function on large frequency band, affecting the proportional excitation between the structure and the accelerometer. In the “system structure + adhesive + accelerometer”, the adhesive may acts like a filter between the source and the sink of vibrations. A variation of the dynamic response of the filter could lead to an erroneous analysis. The authors already investigated the relation between the frequency response function and operating conditions of the test. This paper expands the research by considering the temperature effect in order to depict a complete picture of the adhesive behavior on dynamic response of an accelerometer. A design of experiments (DOE) approach was used to test two bonded aluminum bases at different levels of temperature and frequency of the external sinusoidal excitation, supplied by an electromagnetic shaker. The results clearly demonstrate that the adhesive is not able to change the system response, therefore the signal transmission is good in the entire range of temperature regardless the adhesive chosen. [ABSTRACT FROM AUTHOR]

This paper presents an electromechanical closed-loop interface for the high performance capacitive micro-accelerometer. To overcome the problem that the linearity of the traditional analogue force feedback scheme is sensitive to the mismatch in the MEMS transducer, a pulse-width-modulation (PWM) force feedback method is developed in this paper. Elaborate non-linearity analysis of the PWM force feedback and a comparison with traditional analog feedback method is presented. A prototype interface circuit is designed and fabricated in a standard 0.35 μm CMOS process which measures 6.25 mm. The noise floor of the system is 10 μg/√Hz and the signal bandwidth is 1.2 kHz, respectively. The system shows a DC nonlinearity of 1.6‰ without extra linearization circuit with an input range of ± 1.4 g when interface to a commercial accelerometer device. [ABSTRACT FROM AUTHOR]

This paper presents a micromachined electrochemical angular accelerometer with highly integrated sensitive microelectrodes. Theoretical analyses and numerical simulations were conducted to model the angular accelerometer with key geometrical parameters (e.g., electrode spacing, via spacing and via size) optimized. Highly integrated sensitive microelectrodes were manufactured based on microfabrication and assembled to form MEMS-based electrochemical angular accelerometers. Device characterization was conducted, locating a sensitivity of 80 V/(rad/s2), a bandwidth of 0.01–18 Hz and a noise level of 3.98 × 10−8 (rad/s2)/√Hz. In comparison to a previously reported electrochemical angular microaccelerometer, a significant improvement in sensitivity (80 V/(rad/s2) vs. 10 V/(rad/s2)) was achieved due to the new structure of sensitive microelectrodes. These results indicated the potential of the developed MEMS-based electrochemical angular accelerometer in seismology, including natural disaster monitoring and resource exploration. [ABSTRACT FROM AUTHOR]

During milling operations, wear of cutting tool is inevitable; therefore, tool condition monitoring is essential. One of the difficulties in detecting the state of milling tools is that they are visually inspected, and due to this, the milling process needs to be interrupted. Intelligent monitoring systems based on accelerometers and algorithms have been developed as a part of Industry 4.0 to monitor the tool wear during milling process. In this paper, acoustic emission (AE) and vibration signals captured through sensors are analyzed and the scalograms were constructed from Morlet wavelets. The relative wavelet energy (RWE) criterion was applied to select suitable wavelet functions. Due to the availability of less experimental data to train the LSTM model for the prediction of tool wear, SinGAN was applied to generate additional scalograms and later several image quality parameters were extracted to construct feature vectors. The feature vector is used to train three long short-term memory network (LSTM) models: vanilla, stacked, and bidirectional. To analyze the performance of LSTM models for tool wear prediction, five performance parameters were computed namely R2, adjusted R2, mean absolute error (MAE), root mean square error (RMSE), and mean square error (MSE). The lowest MAE, RMSE, and MSE values were observed as 0.005, 0.016, and 0.0002 and high R2 and Adj. R2 values as 0.997 are observed from the vibration signal. Results suggest that the stacked LSTM model predicts the tool wear better as compared to other LSTM models. The proposed methodology has given very less errors in tool wear predictions and can be extremely useful for the development of an online deep learning tool condition monitoring system. [ABSTRACT FROM AUTHOR]

This paper presents a low power read-out front-end for 3-axis MEMS capacitive accelerometer. The front-end includes the analog preamplifier (to sense the signal coming from the MEMS) and a Successive-Approximation 10b A/D Converter, for digitalization and off-chip digital-signal-processing. Power minimization is achieved by using a continuous-time sensing preamplifier (i.e. constant-charge capacitance-to-voltage conversion) and SAR-ADC with bridge capacitive reduction. Preamplifier programmable in-band gain allows to accommodate different MEMS sensitivities. A very high-impedance MOS transistor is used for MEMS biasing, thus providing very low frequency (<1 Hz) AC coupling. In a 0.13 μm CMOS technology, the full channel consumes 90 μW from a single 1.2 V supply voltage, and achieves an equivalent 67.9 dB@SNR in [1 Hz-4 kHz] bandwidth by exploiting oversampling ratio. [ABSTRACT FROM AUTHOR]

The dataset presented in this paper presents a dataset related to three motionless activities, including driving, watching TV, and sleeping. During these activities, the mobile device may be positioned in different locations, including the pants pockets, in a wristband, over the bedside table, on a table, inside the car, or on other furniture, for the acquisition of accelerometer, magnetometer, gyroscope, GPS, and microphone data. The data was collected by 25 individuals (15 men and 10 women) in different environments in Covilhã and Fundão municipalities (Portugal). The dataset includes the sensors' captures related to a minimum of 2000 captures for each motionless activity, which corresponds to 2.8 h (approximately) for each one. This dataset includes 8.4 h (approximately) of captures for further analysis with data processing techniques, and machine learning methods. It will be useful for the complementary creation of a robust method for the identification of these type of activities. Measurement(s) motion sensors • GPS navigation system • Microphone Device • physical activity Technology Type(s) Smartphone Application • Artificial Intelligence Factor Type(s) raw data Sample Characteristic - Organism Individual Behavior Sample Characteristic - Environment road • house Sample Characteristic - Location Municipality of Fundao • Municipality of Covilha [ABSTRACT FROM AUTHOR]

Mode-localized sensors have attracted attention because of their high parametric sensitivity and first-order common-mode rejection to temperature drift. The high-fidelity detection of resonator amplitude is critical to determining the resolution of mode-localized sensors where the measured amplitude ratio in a system of coupled resonators represents the output metric. Operation at specific bifurcation points in a nonlinear regime can potentially improve the amplitude bias stability; however, the amplitude ratio scale factor to the input measurand in a nonlinear regime has not been fully investigated. This paper theoretically and experimentally elucidates the operation of mode-localized sensors with respect to stiffness perturbations (or an external acceleration field) in a nonlinear Duffing regime. The operation of a mode-localized accelerometer is optimized with the benefit of the insights gained from theoretical analysis with operation in the nonlinear regime close to the top critical bifurcation point. The phase portraits of the amplitudes of the two resonators under different drive forces are recorded to support the experimentally observed improvements for velocity random walk. Employing temperature control to suppress the phase and amplitude variations induced by the temperature drift, 1/f noise at the operation frequency is significantly reduced. A prototype accelerometer device demonstrates a noise floor of 95 ng/√Hz and a bias instability of 75 ng, establishing a new benchmark for accelerometers employing vibration mode localization as a sensing paradigm. A mode-localized accelerometer is first employed to record microseismic noise in a university laboratory environment. [ABSTRACT FROM AUTHOR]

This paper presents a modal parameter estimation of a cantilevered steel beam using low-cost wireless accelerometers for structural health monitoring. To demonstrate the feasibility of using wireless accelerometers, the frequency response functions (FRFs) for the beam are measured. A curve-fitting algorithm is then applied to extract the modal parameters, such as natural frequencies, damping ratios, and mode shapes. These dynamic characteristics of the cantilever beam are acquired and interpreted through the FRFs by the commercial analyzing software. Experimental results show that wireless accelerometers can successfully determine the dynamic properties of the cantilever beam. This paper provides the analytical basis of applying wireless accelerometers to the experimental modal analysis and supports for the feasibility of cantilever beam modal testing using low-cost wireless accelerometers. [ABSTRACT FROM AUTHOR]

This paper proposes a method for estimating the effective zone, including effective depth and effective range of compaction degree, from rapid impact compaction (RIC) on sand layer whose fines content is less than 10%. The proposed method utilizes a string of microelectromechanical system accelerometers to monitor the acceleration at various depths and propagation distances during compaction. To interpret and extract useful information from monitored data, peak-over-threshold (POT) processing and normal distribution function were used to analyze the recorded acceleration. The mean and standard deviation of the threshold peak acceleration were used to evaluate the effective depth and the effective range of compaction degree during RIC compaction. Moreover, spatial contours were used to determine the correlation of the threshold peak acceleration against depth and propagation distance from the RIC impact point. These contours help indicating the distribution of the effect zone after compaction. Lastly, a proposed method is suggested for frequent use in trial tests to quickly determine RIC's required depth and impact spacing. [ABSTRACT FROM AUTHOR]

Abrasive waterjet cutting is a competitive manufacturing technology in the aerospace, defense, and automotive industries. End-user requirements are currently pushing machine builders to improve the automation of their processes, in an effort to reduce costs and downtimes, as well as increase robustness and stability. On this regard, the waterjet focuser is a critical component, as its fast wear progression requires constant human supervision, for promptly detecting detrimental effects on the cutting performance. This paper describes an innovative approach for in-line monitoring the wear progression of a waterjet focuser, by means of an accelerometer installed on its tip. This result is allowed by two separate studies of the focuser, of which the first investigates the sensitivity of its first mode frequency to the wear progression, while the second demonstrates the possibility of tracking said frequency from the in-line vibration signal delivered by the accelerometer, during operation. The presented setup makes use of low-cost sensing hardware that can be easily retrofitted into the design of waterjet focusers. The information delivered is expected to tackle end-user requirements for improved process automation. [ABSTRACT FROM AUTHOR]

Computation of the instantaneous phase and amplitude via the Hilbert Transform is a powerful tool of data analysis. This approach finds many applications in various science and engineering branches but is not proper for causal estimation because it requires knowledge of the signal's past and future. However, several problems require real-time estimation of phase and amplitude; an illustrative example is phase-locked or amplitude-dependent stimulation in neuroscience. In this paper, we discuss and compare three causal algorithms that do not rely on the Hilbert Transform but exploit well-known physical phenomena, the synchronization and the resonance. After testing the algorithms on a synthetic data set, we illustrate their performance computing phase and amplitude for the accelerometer tremor measurements and a Parkinsonian patient's beta-band brain activity. [ABSTRACT FROM AUTHOR]

Silicon-based MEMS (Micro Electromechanical System) transducers have been broadly used to monitor human activities. However, the traditional manufacturing processes are expensive and a production cycle usually takes several months, which cannot meet the demands of personalized customization. This paper presents a rapid fabrication approach for the capacitive accelerometer. The structural components of the accelerometer were fabricated by 3D printing using a PLA (Polylactic acid) filament and the electrodes were deposited by drop-coating of a silver particle-free ink. The silver particle-free ink with a low sintering temperature of 150 °C was synthesized by the complexation of ethanolamine and silver acetate in alcohols. It is shown that it is suitable for metallization of 3D printed polymer structures. As an application of the silver particle-free ink and the fabrication method, a capacitive accelerometer was successfully produced. The measurement results show that the 3D printed accelerometer device has a sensitivity of 46.8 mV/g and a nonlinearity error of 4.06%. The dynamic performances indicate the 3D printed accelerometer device can be used for monitoring human movements in real-time thereby having potential for wearable applications. The work demonstrates a rapid and low-cost method for fabrication of MEMS devices by combining 3D printing and a silver particle-free ink. [ABSTRACT FROM AUTHOR]

Background: The assessment of physical activity levels of hospitalised older people requires accurate and reliable measures. Physical activities that older people in hospital commonly engage in include exercises and walking. Measurement of physical activity levels of older inpatients is essential to evaluate the impact of interventions to improve physical activity levels and to determine associations between physical activity in hospital and other health-related outcome measures. Objective: To determine which measures are used to measure physical activity of older people in hospital, and to describe their properties and applications. Method: A systematic review of four databases: Medline, Embase, CINAHL and AMED was conducted for papers published from 1996 to 2016. Inclusion criteria were participants aged ≥ 65 years and studies which included measures of physical activity in the acute medical inpatient setting. Studies which specifically assessed the activity levels of surgical patients or patients with neurological conditions such as stroke or brain injury were excluded. All study designs were included in the review. Results: 18 studies were included from 127 articles selected for full review. 15 studies used objective measures to measure the physical activity of older inpatients: 11 studies used accelerometers and four used direct systematic observations. Seven accelerometers were identified including the StepWatch Activity Monitor, activPAL, GENEActiv, Kenz Lifecorder EX, Actiwatch-L, Tractivity and AugmenTech Inc. Pittsburgh accelerometer. Three studies used a subjective measure (interviews with nurses and patients) to classify patients into low, intermediate and high mobility groups. The StepWatch Activity Monitor was reported to be most accurate at step-counting in patients with slow gait speed or altered gait. The activPAL was reported to be highly accurate at classifying postures. Conclusion: Physical activity levels of older inpatients can be measured using accelerometers. The accuracy of the accelerometers varies between devices and population-specific validation studies are needed to determine their suitability in measuring physical activity levels of hospitalised older people. Subjective measures are less accurate but can be a practical way of measuring physical activity in a larger group of patients. [ABSTRACT FROM AUTHOR]

To date, non-wear detection algorithms commonly employ a 30, 60, or even 90 mins interval or window in which acceleration values need to be below a threshold value. A major drawback of such intervals is that they need to be long enough to prevent false positives (type I errors), while short enough to prevent false negatives (type II errors), which limits detecting both short and longer episodes of non-wear time. In this paper, we propose a novel non-wear detection algorithm that eliminates the need for an interval. Rather than inspecting acceleration within intervals, we explore acceleration right before and right after an episode of non-wear time. We trained a deep convolutional neural network that was able to infer non-wear time by detecting when the accelerometer was removed and when it was placed back on again. We evaluate our algorithm against several baseline and existing non-wear algorithms, and our algorithm achieves a perfect precision, a recall of 0.9962, and an F1 score of 0.9981, outperforming all evaluated algorithms. Although our algorithm was developed using patterns learned from a hip-worn accelerometer, we propose algorithmic steps that can easily be applied to a wrist-worn accelerometer and a retrained classification model. [ABSTRACT FROM AUTHOR]

In recent years, fingerprint-based positioning has gained researchers' attention since it is a promising alternative to the Global Navigation Satellite System and cellular network-based localization in urban areas. Despite this, the lack of publicly available datasets that researchers can use to develop, evaluate, and compare fingerprint-based positioning solutions constitutes a high entry barrier for studies. As an effort to overcome this barrier and foster new research efforts, this paper presents OutFin, a novel dataset of outdoor location fingerprints that were collected using two different smartphones. OutFin is comprised of diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 reference points. Each site is different in terms of its visibility to the Global Navigation Satellite System and reference points' number, arrangement, and spacing. Before OutFin was made available to the public, several experiments were conducted to validate its technical quality. Measurement(s) outdoor location fingerprints • WiFi data • Bluetooth data • cellular signal strengths • Coordinate Technology Type(s) smartphone • magnetometer • Accelerometer • gyroscope • barometer • ambient light sensor Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13370291 [ABSTRACT FROM AUTHOR]

The Virtual Point Transformation (VPT) makes it possible to experimentally identify the full DoF FRF matrix by projecting the measured displacements onto the Interface Deformation Modes (IDMs). The VP FRFs were already successfully used in Frequency-Based Substructuring (FBS); however, the VPT is susceptible to deviations in the impact location and orientation, as well as to deviations in the sensor's sensitivity and positioning. Uncertainties associated with the sensors can be decreased by using the expanded VPT. This expanded VPT allows the projection of a directly measured rotational response onto the Interface Deformation Modes (IDMs). The consistency of the transformation is achieved by using a rotational weighting matrix, which is formulated to minimize the norm of the overall displacements due to the rotational residual at the VP for each rotational sensor. The rotational response is measured using a direct piezoelectric rotational accelerometer. In this paper the application of the expanded virtual point transformation and the possible advantages are explored on a complex and engineering-like test structure. Both transformations, standard and expanded, are performed for each VP to enable a side-by-side comparison. [ABSTRACT FROM AUTHOR]

Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement. Techniques to classify sleep from the high-resolution accelerometer data primarily rely on heuristic algorithms. In this paper, we explore the potential of detecting sleep using Random forests. Models were trained using data from three different studies where 134 adult participants (70 with sleep disorder and 64 good healthy sleepers) wore an accelerometer on their wrist during a one-night polysomnography recording in the clinic. The Random forests were able to distinguish sleep-wake states with an F1 score of 73.93% on a previously unseen test set of 24 participants. Detecting when the accelerometer is not worn was also successful using machine learning ( F1-score > 93.31 % ), and when combined with our sleep detection models on day-time data provide a sleep estimate that is correlated with self-reported habitual nap behaviour ( r =. 60 ). These Random forest models have been made open-source to aid further research. In line with literature, sleep stage classification turned out to be difficult using only accelerometer data. [ABSTRACT FROM AUTHOR]

This paper presents a precision forward design method for 3D printing (3DP) using kinematic sensitivity via Jacobian matrix (KSJM) considering uncertainty. The advanced manufacturing such as 3DP profoundly affects us everywhere. However, in many cases, practical uncertain factors such as mechanical and thermal effects affect the precision unquantifiably. Aiming at realizing forward design from requirements to performance, the various kinematic chains are summarized to propose the concept of kinematic sensitivity. The Jacobian matrix which reflects the relationship between input error and output error is decomposed through singular value decomposition (SVD). The KSJM is hereby proposed by furtherly defining four sensitivity parameters: compositive amplification factor of error, reliability of error, absolute amplification factor of error, and comprehensive index of error. The influence of uncertainty such as thermal deformation on the accuracy of end-effector in Cartesian space is investigated by heat fluid-solid coupling simulation and piecewise fitting of thermal deformation. Taking delta 3DP robot as example, the kinematic chains and error sensitivity of the multi-body system are addressed. The physical experiment is implemented via attitude sensors with accelerometers, gyroscopes, and magnetometer. The results proved that trajectory accuracy and reliability can be improved especially under complex random uncertainty. [ABSTRACT FROM AUTHOR]

This paper discusses the growth and evolution of residual stresses in (0002) preferentially oriented aluminum nitride (AlN) layers on Si (111) wafers by sputtering technique for the development of micro-electro-mechanical system (MEMS) accelerometer. The microstructure of the deposited films exhibited vertical columnar structures. Residual stresses in the sputtered AlN films are − 1.2 GPa, − 0.8 GPa, and − 0.25 GPa for the film thickness of 600 nm, 750 nm, and 900 nm respectively. The effect of the residual stress on the piezoelectric MEMS acceleration sensor structure is analyzed. The presence of residual stress reduced the resonant frequency (up to 14.72%) and bandwidth (up to 27.3%) of the accelerometer. The locations of the von-Mises stress maxima are shifted from the beam edges (adjoining to the proof-mass) to the whole proof-mass area. The normalized stress sensitivity (which defines the piezoelectric charge sensitivity) is reduced by two orders due to the residual stress in the AlN layers. [ABSTRACT FROM AUTHOR]

This paper introduces a differential vibrating beam MEMS accelerometer demonstrating excellent long-term stability for applications in gravimetry and seismology. The MEMS gravimeter module demonstrates an output Allan deviation of 9 μGal for a 1000 s integration time, a noise floor of 100 μGal/√Hz, and measurement over the full ±1 g dynamic range (1 g = 9.81 ms−2). The sensitivity of the device is demonstrated through the tracking of Earth tides and recording of ground motion corresponding to a number of teleseismic events over several months. These results demonstrate that vibrating beam MEMS accelerometers can be employed for measurements requiring high levels of stability and resolution with wider implications for precision measurement employing other resonant-output MEMS devices such as gyroscopes and magnetometers. [ABSTRACT FROM AUTHOR]

Bearing defects have been accepted as one of the major causes of failure in rotating machinery. It is important to identify and diagnose the failure behavior of bearings for the reliable operation of equipment. In this paper, a low-cost non-contact vibration sensor has been developed for detecting the faults in bearings. The supervised learning method, support vector machine (SVM), has been employed as a tool to validate the effectiveness of the developed sensor. Experimental vibration data collected for different bearing defects under various loading and running conditions have been analyzed to develop a system for diagnosing the faults for machine health monitoring. Fault diagnosis has been accomplished using discrete wavelet transform for denoising the signal. Mahalanobis distance criteria has been employed for selecting the strongest feature on the extracted relevant features. Finally, these selected features have been passed to the SVM classifier for identifying and classifying the various bearing defects. The results reveal that the vibration signatures obtained from developed non-contact sensor compare well with the accelerometer data obtained under the same conditions. A developed sensor is a promising tool for detecting the bearing damage and identifying its class. SVM results have established the effectiveness of the developed non-contact sensor as a vibration measuring instrument which makes the developed sensor a cost-effective tool for the condition monitoring of rotating machines. [ABSTRACT FROM AUTHOR]

MEMS capacitive accelerometers are ubiquitously used in wide-ranging applications. Different applications require a trade-off between design parameters to realize either high sensitivity or precision or wide-dynamic range or speed of response. Planar MEMS structures for sensors usually have a large area compared to thickness or gap. In such structures, squeeze film damping properties of the gas (or air) in the narrow gap significantly affect the dynamic performance of the device. Schemes to reduce the damping effect normally include perforations in the structure to reduce path-lengths of air movement in the narrow gap. But perforations in the structure decrease the mass of the structure leading to a reduction in sensitivity. Therefore, the structural design requires selection of perforation parameters that can provide an optimal trade-off between sensitivity and damping coefficient. This paper discusses our studies through numerical computation when using different configurations of perforations on a typical SOI-based capacitive square accelerometer structure with 1 µm air gap. Both static analysis and analysis at first resonant frequency were carried-out on a range of structures to characterize sensitivity and damping coefficients. The ratio of perforation size versus perforation pitch, η, is used as a basis for sensitivity normalization and studies were carried out to compute damping coefficients for structures with different values of η and count of perforations. Studies reveal a reduction in damping coefficient by 90% to 97% for the η range 0.3 < η < 0.55. The corresponding reduction in effective change in capacitance of the device is limited to the range of 10–25%. [ABSTRACT FROM AUTHOR]

This paper presents a novel control strategy to improve the performance of a low-frequency vibration calibration system. The low-frequency vibration calibration system based on a linear motor is first developed. The amplitude sensitivity and phase sensitivity are then derived. Subsequently, the composite control strategy of the iterative learning control (ILC) and Luenberger observer is established to improve the performance of the linear motor vibration generator. Finally, the frequency stability of the linear motor is measured, and a tri-axial accelerometer is calibrated. Experimental results indicate that the linear motor controlled by the proposed composite strategy can fulfill accelerometer calibration with high precision, and maintain the performance in the full frequency band. [ABSTRACT FROM AUTHOR]

Traditional measurements of gait are typically performed in clinical or laboratory settings where functional assessments are used to collect episodic data, which may not reflect naturalistic gait and activity patterns. The emergence of digital health technologies has enabled reliable and continuous representation of gait and activity in free-living environments. To provide further evidence for naturalistic gait characterization, we designed a master protocol to validate and evaluate the performance of a method for measuring gait derived from a single lumbar-worn accelerometer with respect to reference methods. This evaluation included distinguishing between participants' self-perceived different gait speed levels, and effects of different floor surfaces such as carpet and tile on walking performance, and performance under different bouts, speed, and duration of walking during a wide range of simulated daily activities. Using data from 20 healthy adult participants, we found different self-paced walking speeds and floor surface effects can be accurately characterized. Furthermore, we showed accurate representation of gait and activity during simulated daily living activities and longer bouts of outside walking. Participants in general found that the devices were comfortable. These results extend our previous validation of the method to more naturalistic setting and increases confidence of implementation at-home. [ABSTRACT FROM AUTHOR]

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1113 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 48.2 min (95% limits of agreement (LoA): −50.3 to 146.8 min) for total sleep duration, −17.1 min for REM duration (95% LoA: −56.7 to 91.0 min) and 31.1 min (95% LoA: −67.3 to 129.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with ~100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,262 UK Biobank participants, 1644 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration 6–7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.36; 95% confidence intervals (CIs): 1.18 to 1.58) or high sleep efficiency (HRs: 1.29; 95% CIs: 1.04–1.61). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity. [ABSTRACT FROM AUTHOR]

This paper discusses the design and fabrication of MEMS differential capacitive accelerometer (z-axis sensitive) structure. The accelerometer structure consists of one each movable and reference capacitors in the single accelerometer die fabricated using highly conductive (p-type, resistivity: 0.001 Ω cm) SOI substrate. Resonant frequencies of the designed movable and reference capacitive structures were found to be 9.6 kHz and 150 kHz respectively. Corresponding rest capacitance (at 0 g) of both the capacitors was 2.21 pF. The movable and reference structures showed a deflection of 0.14 µm and 0.6 nm respectively at 50 g applied acceleration. Corresponding changes in capacitances of the movable and reference capacitors were 82.3 fF and < 0.33 fF respectively. The designed accelerometer showed a scale factor sensitivity of the movable capacitor was of ~ 1.65 fF/g. The device demonstrated a dynamic range of in − 17 g to 42 g with a full-scale non-linearity of ~ 3%. Corresponding measured scale factor sensitivity in the centrifuge test was found to be ~ 47 mV/g with an acceleration resolution of ~ 17 mg. The device exhibited cross-axis sensitivity of ~ 2% in the full-scale range. Measured 3 dB bandwidth (380 Hz) of the device matches reasonably with the simulated value (~ 400 Hz). [ABSTRACT FROM AUTHOR]

This paper proposes a novel audio watermarking scheme based on the discrete cosine transform (DCT) and Schur decomposition. The proposed scheme uses the DCT transformation to increase robustness and the Schur decomposition to achieve perceptual transparency. The proposed scheme first applies the DCT transformation to the original audio signal and then applies the Schur decomposition to the mid-frequency band of the DCT coefficients that generate two matrices (U and S). The watermark bits are embedded into the diagonal elements of the triangular matrix S. The Schur decomposition increases the perceptual transparency and the DCT transformation increases robustness of the proposed audio watermarking scheme by effectively resisting several types of audio signal attacks. The imperceptibility of the proposed watermarking scheme is measured subjectively using subjective difference grades (SDG) and objectively using signal-to-noise ratio (SNR) and objective difference grades (ODG) metrics. Its robustness is evaluated against several types of attacks in terms of NC and BER for different types of audio. The resulting of payload capacity, SNR, NC, and BER are as high as 516.26 bps, 77.95, 0.05326, and 0.9727, respectively. Experimental results confirm the proposed scheme is efficient, imperceptible, and robust with a high payload capacity and no effecting audio signal. [ABSTRACT FROM AUTHOR]

MEMS vibrating beam accelerometers use the variation of frequency of a vibrating beam attached to a proof mass to measure its applied acceleration. Considering inertial navigation applications, the beam is actuated in the linear regime to keep high eigen frequency stability. Concerning the new application of MEMS accelerometers as UGS seismometers, the bias stability is not important anymore and the important parameter is the resolution at integration time of 1 ms. It becomes then interesting to actuate the beam in the nonlinear regime. This paper presents a behavioral model of the vibrating beam accelerometer including the nonlinear terms. This model is validated by experimental measurements and phase noise in the nonlinear regime is simulated. The seismometer resolution is then calculated in the nonlinear regime and compared to other state of the art UGS seismometers. [ABSTRACT FROM AUTHOR]

This paper presents a closed-loop interface for micro-machined accelerometer sensor based on a force-feedback sigma-delta loop. A high-order closed-loop accelerometer interface circuit is investigated, and a time-shared multiplexing electrostatic feedback technique is used to eliminate feedthrough between feedback signals and pick-up charge signals. A low noise capacitance detection circuit is proposed with correlated-double-sampling technique to eliminate the 1/f noise and offset of operational amplifier. The interface is fabricated in a standard 0.5 μm CMOS process and the active circuit area is about 13 mm. The chip consumes 20 mW from a 5 V supply with a sampling clock of 250 kHz. The average noise floor of the digital accelerometer is about −115 dBV/Hz over a 1.5 kHz bandwidth. Corresponding to a sensitivity of 302 mV/g, the measured resolution of the accelerometer system is about 6 μg/Hz. The nonlinearity is 0.085 %. The figure of merit shows that the proposed sigma-delta interface achieves a good performance. [ABSTRACT FROM AUTHOR]

Sigma-delta modulation has been widely used in micro-machined accelerometers. Previous researches are mainly focused on increasing the order of the sigma-delta modulator for the mechanical sensor to improving the performance of the micro-machined accelerometers. These designs performed well in high resolution acceleration measurement, but they are insufficient in balancing the proof mass quickly while the micro-machined accelerometer is working. In this paper, the design of order-adjustable micro-machined accelerometer is proposed. It employs a mechanical sensor to balance the proof mass, and a fifth order MEMS accelerator to measure the acceleration with high resolution. Furthermore, this work shows an optimized design with a SQNR of 156.5 and 73.6 dB which can accurately measure the acceleration input and quickly balance the proof mass, respectively. [ABSTRACT FROM AUTHOR]

This paper presents convection behavior investigation of CMOS MEMS convective accelerometers using both analytical and FEM techniques. In a first part, a newly developed accelerometer 3D model is used in FEM simulations to model convection behavior as a function of design geometry and temperature. Using various sizes of two different cover shapes, sensitivity reading and its maximum position in cavity are found to be largely affected by both cover size and shape. In addition, a sensor with cavity width of 600 μm produces sensitivity saturation starting at a cavity depth of 200 μm, for both cover shapes. Using FEM data and curve fitting, differential temperature is claimed to be linearly linked to the effective heater temperature to the power of 1.7. Using the same cavity design and from computed heating efficiency values, we found that a 60 μm width heater offers the best efficiency. This cavity and heater designs give an optimal detector position of 120 μm from heater center along the sensitive axis. Moreover, dual axis accelerometers are found to be more power efficient than single axis ones. In the second part, we present Hardee's spherical model and investigate its possible application on convective accelerometers. It is shown that inner and outer isotherms deformation, caused by accelerometer design and convection process, should be modeled by including sensor geometry parameters in the derived governing equations. Moreover, Hardee's biasing temperature relation has to be revised if it is to be used for convective accelerometers. [ABSTRACT FROM AUTHOR]

Continued advances in microelectromechanical systems (MEMS) technology have led to development of a multitude of new sensors and their corresponding advanced applications. Great many of these sensors (e.g., microgyroscopes, accelerometers, biological, chemical, security, medical, etc.) rely on either sensing elements or elastic suspensions that resonate. Regardless of their applications, sensors are always designed to provide the most exact responses to the signals they are developed to detect and/or monitor. One way to quantify this exactness is to use the Quality factor ( Q-factor). MEMS sensors are typically fabricated out of materials that are mechanically sound at the microscale, but can be relatively poor electrical conductors. For this reason, areas of MEMS are coated with various thin metal films to provide electrical pathways. These films, however, adversely alter resonant properties of a device. To facilitate our study, microcantilever configurations were selected to test influence that thin metal films have on resonators. This paper reviews a theoretical analysis of the effect that thermoelastic internal friction has on the Q-factor of microscale resonators and shows that the internal friction relating to TED is a fundamental damping mechanism in determination of quality of high-Q resonators over a range of operating conditions. Using silicon microcantilevers coated with aluminum films from 5 nm to 30 nm thick, on one as well as both sides, Q-factors were experimentally determined using the ring-down method. From the ring-down curve, the Q-factor of each microcantilever was determined. Experimental results show that as thickness of the aluminum film increases, Q-factor of the device decreases. Comparison of analytical and experimental results indicates good correlation, well within the limits based on uncertainty analysis. In addition, preliminary results also show a significant temperature dependence of the Q-factor of aluminum coated microcantilevers. [ABSTRACT FROM AUTHOR]

For manual pipe welding process, welding position and speed are two of the most important welding parameters. Highly skilled pipe welders can well adjust their operation to produce high-quality welds. However, even experienced welders may sometimes have defected welds due to improper welding speed. The weld quality will be better guaranteed if the welding position and speed can be measured and other welding parameters can be changed accordingly. This paper introduced the development and experiment results on a practical accelerometer-based torch sensor to measure torch position and speed. Based on the measured acceleration data, the two important welding parameters were calculated with moderate computing load by an embedded control system. For future work, the welding position and speed calculation with sufficient accuracy can help the design of advanced control algorithm to various pipe welding processes. [ABSTRACT FROM AUTHOR]

Digital health technologies (DHTs) are increasingly being adopted in clinical trials, as they enable objective evaluations of health parameters in free-living environments. Although lumbar accelerometers notably provide reliable gait parameters, embedding accelerometers in chest devices, already used for vital signs monitoring, could capture a more comprehensive picture of participants' wellbeing, while reducing the burden of multiple devices. Here we assess the validity of gait parameters measured from a chest accelerometer. Twenty healthy adults (13 females, mean ± sd age: 33.9 ± 9.1 years) instrumented with lumbar and chest accelerometers underwent in-lab and outside-lab walking tasks, while monitored with reference devices (an instrumented mat, and a 6-accelerometers set). Gait parameters were extracted from chest and lumbar accelerometers using our open-source Scikit Digital Health gait (SKDH-gait) algorithm, and compared against reference values via Bland–Altman plots, Pearson's correlation, and intraclass correlation coefficient. Mixed effects regression models were performed to investigate the effect of device, task, and their interaction. Gait parameters derived from chest and lumbar accelerometers showed no significant difference and excellent agreement across all tasks, as well as good-to-excellent agreement and strong correlation against reference values, thus supporting the deployment of a single multimodal chest device in clinical trials, to simultaneously measure gait and vital signs. Trial Registration: The study was reviewed and approved by the Advarra IRB (protocol number: Pro00043100). [ABSTRACT FROM AUTHOR]

Piezoelectric accelerometers excel in vibration sensing. In the emerging trend of fully organic electronic microsystems, polymeric piezoelectric accelerometers can be used as vital front-end components to capture dynamic signals, such as vocal vibrations in wearable speaking assistants for those with speaking difficulties. However, high-performance polymeric piezoelectric accelerometers suitable for such applications are rare. Piezoelectric organic compounds such as PVDF have inferior properties to their inorganic counterparts such as PZT. Consequently, most existing polymeric piezoelectric accelerometers have very unbalanced performance metrics. They often sacrifice resonance frequency and bandwidth for a flat-band sensitivity comparable to those of PZT-based accelerometers, leading to increased noise density and limited application potentials. In this study, a new polymeric piezoelectric accelerometer design to overcome the material limitations of PVDF is introduced. This new design aims to simultaneously achieve high sensitivity, broad bandwidth, and low noise. Five samples were manufactured and characterized, demonstrating an average sensitivity of 29.45 pC/g within a ± 10 g input range, a 5% flat band of 160 Hz, and an in-band noise density of 1.4 µg/ Hz . These results surpass those of many PZT-based piezoelectric accelerometers, showing the feasibility of achieving comprehensively high performance in polymeric piezoelectric accelerometers to increase their potential in novel applications such as organic microsystems. [ABSTRACT FROM AUTHOR]

Accurate physical activity monitoring is essential to understand the impact of physical activity on one's physical health and overall well-being. However, advances in human activity recognition algorithms have been constrained by the limited availability of large labelled datasets. This study aims to leverage recent advances in self-supervised learning to exploit the large-scale UK Biobank accelerometer dataset—a 700,000 person-days unlabelled dataset—in order to build models with vastly improved generalisability and accuracy. Our resulting models consistently outperform strong baselines across eight benchmark datasets, with an F1 relative improvement of 2.5–130.9% (median 24.4%). More importantly, in contrast to previous reports, our results generalise across external datasets, cohorts, living environments, and sensor devices. Our open-sourced pre-trained models will be valuable in domains with limited labelled data or where good sampling coverage (across devices, populations, and activities) is hard to achieve. [ABSTRACT FROM AUTHOR]

In this paper, a monitoring and diagnosis system based on neural network model is proposed (gear bearing-combined faults). Data sets are collected from vibration signals, measured by pseudo-electric accelerometer fixed on various measuring points of an experimental setup (gearbox test rig designed and manufactured especially for this work). Time and frequency analyses are then conducted to determine statistical parameters (root mean square (RMS) value, mean square value, variance, kurtosis factor, crest factor, and clearance factor or margin factor) and spectrums (fast Fourier transform). The time domain parameters and the defects of binary codes are used as input and output data, to train and test the neural network model. Moreover, the L27 Taguchi standard orthogonal array and Grey-Taguchi method are used as multi-objective optimization approaches, to find the best neural network model architecture. The results show the applicability and effectiveness, of the proposed system, for monitoring and diagnosis of rotating machinery. They can be extended efficiently to study other faults and mechanisms. [ABSTRACT FROM AUTHOR]

Pre-shaped microbeams, curved or inclined, are widely used in MEMS for their interesting stiffness properties. These mechanisms allow a wide range of positive and negative stiffness tuning in their direction of motion. A mechanism of pre-shaped beams with opposite curvature, connected in a parallel configuration, can be electrothermally tuned to reach a near-zero or negative stiffness behavior at the as-fabricated position. The simple structure helps incorporate the tunable spring mechanism in different designs for accelerometers, even with different transduction technologies. The sensitivity of the accelerometer can be considerably increased or tuned for different applications by electrothermally changing the stiffness of the spring mechanism. Opposite inclined beams are implemented in a capacitive micromachined accelerometer. The measurements on fabricated prototypes showed more than 55 times gain in sensitivity compared to their initial sensitivity. The experiments showed promising results in enhancing the resolution of acceleration sensing and the potential to reach unprecedent performance in micromachined accelerometers. [ABSTRACT FROM AUTHOR]

Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings. [ABSTRACT FROM AUTHOR]

Annotating accelerometer-based physical activity data remains a challenging task, limiting the creation of robust supervised machine learning models due to the scarcity of large, labeled, free-living human activity recognition (HAR) datasets. Researchers are exploring self-supervised learning (SSL) as an alternative to relying solely on labeled data approaches. However, there has been limited exploration of the impact of large-scale, unlabeled datasets for SSL pre-training on downstream HAR performance, particularly utilizing more than one accelerometer. To address this gap, a transformer encoder network is pre-trained on various amounts of unlabeled, dual-accelerometer data from the HUNT4 dataset: 10, 100, 1k, 10k, and 100k hours. The objective is to reconstruct masked segments of signal spectrograms. This pre-trained model, termed SelfPAB, serves as a feature extractor for downstream supervised HAR training across five datasets (HARTH, HAR70+, PAMAP2, Opportunity, and RealWorld). SelfPAB outperforms purely supervised baselines and other SSL methods, demonstrating notable enhancements, especially for activities with limited training data. Results show that more pre-training data improves downstream HAR performance, with the 100k-hour model exhibiting the highest performance. It surpasses purely supervised baselines by absolute F1-score improvements of 7.1% (HARTH), 14% (HAR70+), and an average of 11.26% across the PAMAP2, Opportunity, and RealWorld datasets. Compared to related SSL methods, SelfPAB displays absolute F1-score enhancements of 10.4% (HARTH), 18.8% (HAR70+), and 16% (average across PAMAP2, Opportunity, RealWorld). [ABSTRACT FROM AUTHOR]

The paper describes a table-type NMR-relaxometer for measuring spin-spin relaxation times of protons in living tissues. The proton spin relaxation times depend on the average number of paramagnetic centers in the medium under study; in particular, they are indicative of the difference in the intensity of the metabolic processes in the cellular structures of muscle and adipose tissues. This technique allows detecting age-related changes (sarcopenia) associated with the replacement of muscle fibers with connective tissue. Such relaxometers are comfortable to use and hold promise for assessing fatty infiltration of muscle tissue. [ABSTRACT FROM AUTHOR]