MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: a review on research activities in Italy

Development of an IMU based 2-segment foot model for an applicable medical gait analysis

BACKGROUND

The two most commonly instrumented gait analysis tools used are Optical Motion Capture systems (OMC) and Inertial Measurement Units (IMU). To date, OMC based gait analysis is considered the gold-standard. Still, it is space-, cost-, and time-intense. On the other hand IMU systems are more cost- and time effective but simulate the whole foot as a single segment. To get a more detailed model of the foot and ankle, a new 2-segment foot model using IMU was developed, comparable to the multi-segment foot models assessed by OMC.

RESEARCH QUESTION

Can an IMU based 2-segment foot model be developed to provide a more detailed representation of the foot and ankle kinematics?

METHODS

To establish a 2-segment foot model, in addition to the previous 1-segment foot model an IMU sensor was added to the calcaneus. This allowed the differentiation between the hindfoot and forefoot kinematics. 30 healthy individuals (mean age 27 ± 7 years) were recruited to create a norm data set of a healthy cohort. Moreover, the kinematic data of the 2-segment foot model were compared to those of the traditional 1-segment foot model using statistical parametric mapping.

RESULTS

The 2-segment foot model proved to be applicable. Furthermore, it allowed for a more detailed representation of the foot and ankle joints, similar to other multi-segment foot model. The healthy cohort's norm data set showed a homogeneous motion pattern for gait.

CONCLUSION

The 2-segment foot model allows for an extension of IMU-based gait analysis. Futures studies must prove the reliability and validity of the 2-segment foot model in healthy and pathologic situations.

LEVEL OF EVIDENCE

Level II.

The effects of mobile technology-based support on young women with depressive symptoms: A block randomized controlled trial

BACKGROUND

The current body of knowledge highlights the potential role of mobile technology as a medium to deliver support for psychological and physical health. This study evaluated the influence of mobile technology support on depressive symptoms and physical activity in female university students.

METHODS

A block randomized controlled trial design with a single site was used. Ninety-nine participants were block-randomized into 3 arms: Experimental Group 1 (emotional and informational support group), Experimental Group 2 (informational support group), and the control group. Interventions were delivered via mobile technology for 2 weeks. Data on depressive symptoms and physical activity were collected from 84 participants at baseline and on Days 8 and 15. Data analyses included descriptive statistics, t tests, one-way analysis of variance, and repeated-measures analysis of variance.

RESULTS

This study showed no interaction effect of time and group on depressive symptom scores and physical activity, considering the emotional and informational support from mobile technology. However, Experimental Group 1 exhibited a significant reduction in depressive symptoms during the first week of the study compared to Experimental Group 2 and the control group. While physical activity in Experimental Group 2 and control group increased only during the first week of the study and subsequently decreased, Experimental Group 1 showed an initial increase during the first week that was sustained into the second week.

CONCLUSIONS

Since informational and emotional support showed a strong effect over a short period of time, mobile technology offering emotional support could be used to provide crisis interventions for depression among young women when a short-term impact is required.

Power Electronics Revolutionized: A Comprehensive Analysis of Emerging Wide and Ultrawide Bandgap Devices

This article provides a comprehensive review of wide and ultrawide bandgap power electronic semiconductor devices, comparing silicon (Si), silicon carbide (SiC), gallium nitride (GaN), and the emerging device diamond technology. Key parameters examined include bandgap, critical electric field, electron mobility, voltage/current ratings, switching frequency, and device packaging. The historical evolution of each material is traced from early research devices to current commercial offerings. Significant focus is given to SiC and GaN as they are now actively competing with Si devices in the market, enabled by their higher bandgaps. The paper details advancements in material growth, device architectures, reliability, and manufacturing that have allowed SiC and GaN adoption in electric vehicles, renewable energy, aerospace, and other applications requiring high power density, efficiency, and frequency operation. Performance enhancements over Si are quantified. However, the challenges associated with the advancements of these devices are also elaborately described: material availability, thermal management, gate drive design, electrical insulation, and electromagnetic interference. Alongside the cost reduction through improved manufacturing, material availability, thermal management, gate drive design, electrical insulation, and electromagnetic interference are critical hurdles of this technology. The review analyzes these issues and emerging solutions using advanced packaging, circuit integration, novel cooling techniques, and modeling. Overall, the manuscript provides a timely, rigorous examination of the state of the art in wide bandgap power semiconductors. It balances theoretical potential and practical limitations while assessing commercial readiness and mapping trajectories for further innovation. This article will benefit researchers and professionals advancing power electronic systems.

Recognizing Human Activity of Daily Living Using a Flexible Wearable for 3D Spine Pose Tracking

The World Health Organization recognizes physical activity as an influencing domain on quality of life. Monitoring, evaluating, and supervising it by wearable devices can contribute to the early detection and progress assessment of diseases such as Alzheimer's, rehabilitation, and exercises in telehealth, as well as abrupt events such as a fall. In this work, we use a non-invasive and non-intrusive flexible wearable device for 3D spine pose measurement to monitor and classify physical activity. We develop a comprehensive protocol that consists of 10 indoor, 4 outdoor, and 8 transition states activities in three categories of static, dynamic, and transition in order to evaluate the applicability of the flexible wearable device in human activity recognition. We implement and compare the performance of three neural networks: long short-term memory (LSTM), convolutional neural network (CNN), and a hybrid model (CNN-LSTM). For ground truth, we use an accelerometer and strips data. LSTM reached an overall classification accuracy of 98% for all activities. The CNN model with accelerometer data delivered better performance in lying down (100%), static (standing = 82%, sitting = 75%), and dynamic (walking = 100%, running = 100%) positions. Data fusion improved the outputs in standing (92%) and sitting (94%), while LSTM with the strips data yielded a better performance in bending-related activities (bending forward = 49%, bending backward = 88%, bending right = 92%, and bending left = 100%), the combination of data fusion and principle components analysis further strengthened the output (bending forward = 100%, bending backward = 89%, bending right = 100%, and bending left = 100%). Moreover, the LSTM model detected the first transition state that is similar to fall with the accuracy of 84%. The results show that the wearable device can be used in a daily routine for activity monitoring, recognition, and exercise supervision, but still needs further improvement for fall detection.

A Differential Inertial Wearable Device for Breathing Parameter Detection: Hardware and Firmware Development, Experimental Characterization

Breathing monitoring is crucial for evaluating a patient's health status. The technologies commonly used to monitor respiration are costly, bulky, obtrusive, and inaccurate, mainly when the user moves. Consequently, efforts have been devoted to providing new solutions and methodologies to overcome these limitations. These methods have several uses, including healthcare monitoring, measuring athletic performance, and aiding patients with respiratory diseases, such as COPD (chronic obtrusive pulmonary disease), sleep apnea, etc. Breathing-induced chest movements can be measured noninvasively and discreetly using inertial sensors. This research work presents the development and testing of an inertia-based chest band for breathing monitoring through a differential approach. The device comprises two IMUs (inertial measurement units) placed on the patient's chest and back to determine the differential inertial signal, carrying out information detection about the breathing activity. The chest band includes a low-power microcontroller section to acquire inertial data from the two IMUs and process them to extract the breathing parameters (i.e., RR-respiration rate; TI/TE-inhalation/exhalation time; IER-inhalation-to-exhalation time; V-flow rate), using the back IMU as a reference. A BLE transceiver wirelessly transmits the acquired breathing parameters to a mobile application. Finally, the test results demonstrate the effectiveness of the used dual-inertia solution; correlation and Bland-Altman analyses were performed on the RR measurements from the chest band and the reference, demonstrating a high correlation (r¯ = 0.92) and low mean difference (MD¯ = -0.27 BrPM (breaths per minute)), limits of agreement (LoA¯ = +1.16/-1.75 BrPM), and mean absolute error (MAE¯ = 1.15%). Additionally, the experimental results demonstrated that the developed device correctly measured the other breathing parameters (TI, TE, IER, and V), keeping an MAE of ≤5%. The obtained results indicated that the developed chest band is a viable solution for long-term breathing monitoring, both in stationary and moving users.

Validity and reliability of inertial measurement units measurements for running kinematics in different foot strike pattern runners

This study aimed to assess the validity and reliability of the three-dimensional joint kinematic outcomes obtained by the inertial measurement units (IMUs) for runners with rearfoot strike pattern (RFS) and non-rearfoot strike pattern (NRFS). The IMUs system and optical motion capture system were used to simultaneous collect 3D kinematic of lower extremity joint data from participants running at 12 km/h. The joint angle waveforms showed a high correlation between the two systems after the offset correction in the sagittal plane (NRFS: coefficient of multiple correlation (CMC) = 0.924-0.968, root mean square error (RMSE) = 4.6^°-13.7^°; RFS: CMC = 0.930-0.965, RMSE = 3.1^°-7.7^°), but revealed high variability in the frontal and transverse planes (NRFS: CMC = 0.924-0.968, RMSE = 4.6°-13.7°; RFS: CMC = 0.930-0.965, RMSE = 3.1°-7.7°). The between-rater and between-day reliability were shown to be very good to excellent in the sagittal plane (between-rater: NRFS: CMC = 0.967-0.975, RMSE = 1.9°-2.9°, RFS: CMC = 0.922-0.989, RMSE = 1.0°-2.5°; between-day: NRFS: CMC = 0.950-0.978, RMSE = 1.6°-2.7°, RFS: CMC = 0.920-0.989, RMSE = 1.7°-2.2°), whereas the reliability was weak to very good (between-rater: NRFS: CMC = 0.480-0.947, RMSE = 1.1°-2.7°, RFS: CMC = 0.646-0.873, RMSE = 0.7°-2.4°; between-day: NRFS: CMC = 0.666-0.867, RMSE = 0.7°-2.8°, RFS: CMC = 0.321-0.805, RMSE = 0.9°-5.0°) in the frontal and transverse planes across all joints in both types of runners. The IMUs system was a feasible tool for measuring lower extremity joint kinematics in the sagittal plane during running, especially for RFS runners. However, the joint kinematics data in frontal and transverse planes derived by the IMUs system need to be used with caution.

Fabrication of Vertical MEMS Actuator with Hollow Square Electrode for SPR Sensing Applications

In this study, an electrostatically driven vertical MEMS actuator was designed using a hollow square electrode. To attain vertical actuation, a hollow square-shaped electrode was designed on the glass substrate. The silicon proof mass, containing a step, was utilized to realize analogue actuation without pull-in. The vertical MEMS actuator was fabricated using the SiOG (Silicon on Glass) process and the total actuator size was 8.3 mm × 8.3 mm. The fabricated proof mass was freestanding due to eight serpentine springs with 30 μm width. The vertical movement of the MEMS actuator was successfully controlled electrostatically. The measured vertical movement was 5.6 µm for a voltage of 40 V, applied between the top silicon structure and the hollow square electrode. The results shown here confirm that the proposed MEMS actuator was able to control the vertical displacement using an applied voltage.

Validity and Reliability of Inertial Measurement Units on Lower Extremity Kinematics During Running: A Systematic Review and Meta-Analysis

Background

Inertial measurement units (IMUs) are useful in monitoring running and alerting running-related injuries in various sports settings. However, the quantitative summaries of the validity and reliability of the measurements from IMUs during running are still lacking. The purpose of this review was to investigate the concurrent validity and test–retest reliability of IMUs for measuring gait spatiotemporal outcomes and lower extremity kinematics of health adults during running.

Methods

PubMed, CINAHL, Embase, Scopus and Web of Science electronic databases were searched from inception until September 2021. The inclusion criteria were as follows: (1) evaluated the validity or reliability of measurements from IMUs, (2) measured specific kinematic outcomes, (3) compared measurements using IMUs with those obtained using reference systems, (4) collected data during running, (5) assessed human beings and (6) were published in English. Eligible articles were reviewed using a modified quality assessment. A meta-analysis was performed to assess the pooled correlation coefficients of validity and reliability.

Results

Twenty-five articles were included in the systematic review, and data from 12 were pooled for meta-analysis. The methodological quality of studies ranged from low to moderate. Concurrent validity is excellent for stride length (intraclass correlation coefficient (ICC) (95% confidence interval (CI)) = 0.937 (0.859, 0.972), p < 0.001), step frequency (ICC (95% CI) = 0.926 (0.896, 0.948), r (95% CI) = 0.989 (0.957, 0.997), p  < 0.001) and ankle angle in the sagittal plane (r (95% CI) = 0.939 (0.544, 0.993), p = 0.002), moderate to excellent for stance time (ICC (95% CI) = 0.664 (0.354, 0.845), r (95% CI) = 0.811 (0.701, 0.881), p < 0.001) and good for running speed (ICC (95% CI) = 0.848 (0.523, 0.958), p = 0.0003). The summary Fisher's Z value of flight time was not statistically significant (p = 0.13). Similarly, the stance time showed excellent test–retest reliability (ICC (95% CI) = 0.954 (0.903, 0.978), p < 0.001) and step frequency showed good test–retest reliability (ICC (95% CI) = 0.896 (0.837, 0.933), p < 0.001).

Conclusions

Findings in the current review support IMUs measurement of running gait spatiotemporal parameters, but IMUs measurement of running kinematics on lower extremity joints needs to be reported with caution in healthy adults.

Trial Registration: PROSPERO Registration Number: CRD42021279395.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40798-022-00477-0.

MEMS-Based Tactile Sensors: Materials, Processes and Applications in Robotics

Commonly encountered problems in the manipulation of objects with robotic hands are the contact force control and the setting of approaching motion. Microelectromechanical systems (MEMS) sensors on robots offer several solutions to these problems along with new capabilities. In this review, we analyze tactile, force and/or pressure sensors produced by MEMS technologies including off-the-shelf products such as MEMS barometric sensors. Alone or in conjunction with other sensors, MEMS platforms are considered very promising for robots to detect the contact forces, slippage and the distance to the objects for effective dexterous manipulation. We briefly reviewed several sensing mechanisms and principles, such as capacitive, resistive, piezoresistive and triboelectric, combined with new flexible materials technologies including polymers processing and MEMS-embedded textiles for flexible and snake robots. We demonstrated that without taking up extra space and at the same time remaining lightweight, several MEMS sensors can be integrated into robotic hands to simulate human fingers, gripping, hardness and stiffness sensations. MEMS have high potential of enabling new generation microactuators, microsensors, micro miniature motion-systems (e.g., microrobots) that will be indispensable for health, security, safety and environmental protection.

Fabrication and Evaluation of a Flexible MEMS-Based Microthermal Flow Sensor

Based on the results of computational fluid dynamics simulations, this study designed and fabricated a flexible thermal-type micro flow sensor comprising one microheater and two thermistors using a micro-electromechanical system (MEMS) process on a flexible polyimide film. The thermistors were connected to a Wheatstone bridge circuit, and the resistance difference between the thermistors resulting from the generation of a flow was converted into an output voltage signal using LabVIEW software. A mini tube flow test was conducted to demonstrate the sensor's detection of fluid velocity in gas and liquid flows. A good correlation was found between the experimental results and the simulation data. However, the results for the gas and liquid flows differed in that for gas, the output voltage increased with the fluid's velocity and decreased against the liquid's flow velocity. This study's MEMS-based flexible microthermal flow sensor achieved a resolution of 1.1 cm/s in a liquid flow and 0.64 cm/s in a gas flow, respectively, within a fluid flow velocity range of 0-40 cm/s. The sensor is suitable for many applications; however, with some adaptations to its electrical packaging, it will be particularly suitable for detecting biosignals in healthcare applications, including measuring respiration and body fluids.

A Novel Parallel Processing Model for Noise Reduction and Temperature Compensation of MEMS Gyroscope

To eliminate the noise and temperature drift in an Micro-Electro-Mechanical Systems (MEMS) gyroscope's output signal for improving measurement accuracy, a parallel processing model based on Multi-objective particle swarm optimization based on variational modal decomposition-time-frequency peak filter (MOVMD-TFPF) and Beetle antennae search algorithm- Elman neural network (BAS-Elman NN) is established. Firstly, variational mode decomposition (VMD) is optimized by multi-objective particle swarm optimization (MOPSO); then, the best decomposition parameters [k_best,a_best] can be obtained. Secondly, the gyroscope output signals are decomposed by VMD optimized by MOPSO (MOVMD); then, the intrinsic mode functions (IMFs) obtained after decomposition are classified into a noise segment, mixed segment, and drift segment by sample entropy (SE). According to the idea of a parallel model, the noise segment can be discarded directly, the mixed segment is denoised by time-frequency peak filtering (TFPF), and the drift segment is compensated at the same time. In the compensation part, the beetle antennae search algorithm (BAS) is adopted to optimize the network parameters of the Elman neural network (Elman NN). Subsequently, the double-input/single-output temperature compensation model based on the BAS-Elman NN is established to compensate the drift segment, and these processed segments are reconstructed to form the final gyroscope output signal. Experimental results demonstrate the superiority of this parallel processing model; the angle random walk of the compensated gyroscope output is decreased from 0.531076 to 5.22502 × 10^-3°/h/√Hz, and its bias stability is decreased from 32.7364°/h to 0.140403°/h, respectively.

Review of Wearable Devices and Data Collection Considerations for Connected Health

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.

Atomic Simulations of Packing Structures, Local Stress and Mechanical Properties for One Silicon Lattice with Single Vacancy on Heating

The effect of vacancy defects on the structure and mechanical properties of semiconductor silicon materials is of great significance to the development of novel microelectronic materials and the processes of semiconductor sensors. In this paper, molecular dynamics is used to simulate the atomic packing structure, local stress evolution and mechanical properties of a perfect lattice and silicon crystal with a single vacancy defect on heating. In addition, their influences on the change in Young’s modulus are also analyzed. The atomic simulations show that in the lower temperature range, the existence of vacancy defects reduces the Young’s modulus of the silicon lattice. With the increase in temperature, the local stress distribution of the atoms in the lattice changes due to the migration of the vacancy. At high temperatures, the Young’s modulus of the silicon lattice changes in anisotropic patterns. For the lattice with the vacancy, when the temperature is higher than 1500 K, the number and degree of distortion in the lattice increase significantly, the obvious single vacancy and its adjacent atoms contracting inward structure disappears and the defects in the lattice present complex patterns. By applying uniaxial tensile force, it can be found that the temperature has a significant effect on the elasticity–plasticity behaviors of the Si lattice with the vacancy.

Construction and Piezoelectric Properties of a Single-Peptide Nanotube Composed of Cyclic β-peptides with Helical Peptides on the Side Chains

To develop nanopiezoelectronics, it is necessary to investigate the relationship between the sizes and piezoelectric properties of the material. Peptide nanotubes (PNTs) composed of cyclic β-peptides have been studied as leading candidates for nanopiezoelectric materials. The current drawback of PNTs is aggregation to form a PNT bundle structure due to strong dipole-dipole interactions between PNTs. Here, we report the construction and piezoelectric properties of single PNTs without nonspecific aggregation by side-chain modification of helical peptides. A cyclic tri-β-peptide with a helical peptide was prepared by multiple-step liquid-phase peptide synthesis and assembled into PNTs by the vapor diffusion method. These nanotubes were characterized by polarized light microscopy and Fourier transform infrared (FTIR) spectroscopy. Additionally, atomic force microscopy (AFM) topographic images showed nanotubes with a height of 4 nm, which corresponds to the diameter of a PNT on a gold-coated mica substrate, indicating that a single PNT was prepared successfully. The converted piezoelectric response of a single PNT was determined to be 1.39 ± 0.12 pm/V. This value was consistent with that of a PNT bundle, which reveals that the piezoelectricity of PNTs is induced by deformation of their cyclic skeletons and is independent of the bundled structure. This finding not only demonstrates a new molecular design strategy to construct these smallest piezoelectric biomaterials by controlling the supramolecular hierarchical structures but also provides insights into the correlation between molecular assembly morphology and size-dependent piezoelectric properties.

Towards User-Friendly Wearable Platforms for Monitoring Unconstrained Indoor and Outdoor Activities

Developing wearable platforms for unconstrained monitoring of limb movements has been an active recent topic of research due to potential applications such as clinical and athletic performance evaluation. However, practicality of these platforms might be affected by the dynamic and complexity of movements as well as characteristics of the surrounding environment. This paper addresses such issues by proposing a novel method for obtaining kinematic information of joints using a custom-designed wearable platform. The proposed method uses data from two gyroscopes and an array of textile stretch sensors to accurately track three-dimensional movements, including extension, flexion, and rotation, of a joint. More specifically, gyroscopes provide angular velocity data of two sides of a joint, while their relative orientation is estimated by a machine learning algorithm. An Unscented Kalman Filter (UKF) algorithm is applied to directly fuse angular velocity/relative orientation data and estimate the kinematic orientation of the joint. Experimental evaluations were carried out using data from 10 volunteers performing a series of predefined as well as unconstrained random three-dimensional trunk movements. Results show that the proposed sensor setup and the UKF-based data fusion algorithm can accurately estimate the orientation of the trunk relative to pelvis with an average error of less than 1.72 degrees in predefined movements and a comparable accuracy of 3.00 degrees in random movements. Moreover, the proposed platform is easy to setup, does not restrict body motion, and is not affected by environmental disturbances. This study is a further step towards developing user-friendly wearable sensor systems than can be readily used in indoor and outdoor settings without requiring bulky equipment or a tedious calibration phase.

The Role of the Internet of Things in Healthcare: Future Trends and Challenges

BACKGROUND AND OBJECTIVE

With the recent advances in the Internet of Things (IoT), the field has become more and more developed in healthcare. The Internet of things will help physicians and hospital staff perform their duties comfortably and intelligently. With the latest advanced technologies, most of the challenges of using IoT have been resolved, and this technology can be a great revolution and has many benefits in the future of digital. Healthcare is one of the most useful areas for IoT use. The most important application of IoT is to monitor and make quick decisions in critical situations. Thanks to this technology-based treatment approach, there is an unprecedented opportunity to better the quality and productivity of treatments and better the patient's well-being and better government funding.

METHODS

In this paper, we provide a comprehensive overview of the primary uses of IoT in healthcare. We used the Systematic Literature Review (SLR) method to analyze and comparison articles published in this field between 2015 and March 2020.

RESULTS

A comprehensive taxonomy is presented based on the contents of the articles under study. In this article, a brief overview of selected articles based on research questions is given and highlights the most critical challenges and case studies for the future use of IoT in healthcare.

CONCLUSIONS

According to a detailed study of the 89 articles and a glimpse into about 208 articles, challenges and future trends in healthcare have been identified.

Theoretical and Experimental Study on Nonlinear Failure of an MEMS Accelerometer under Dual Frequency Acoustic Interference

In order to quantitatively study the interfered output of the accelerometer under an acoustic injection attack, a mathematical model for fitting and predicting the accelerometer output was proposed. With ADXL103 as an example, an acoustic injection attack experiment with amplitude sweeping and frequency sweeping was performed. In the mathematical model, the R-squared coefficient was R² = 0.9990 in the acoustic injection attack experiment with amplitude sweeping, and R² = 0.9888 with frequency sweeping. Based on the mathematical model, the dual frequency acoustic injection attack mode was proposed. The difference frequency signal caused by the nonlinear effect was not filtered by the low-pass filter. At a 115 dB sound pressure level, the maximum acceleration bias of the output was 4.4 m/s² and the maximum amplitude of fluctuation was 4.97 m/s². Two kinds of methods of prevention against acoustic injection attack were proposed, including changing the damping ratio of the accelerometer and adding a preposition low-pass filter.

A Filtering Algorithm of MEMS Gyroscope to Resist Acoustic Interference

To reduce the impact of acoustic interference in a microelectromechanical system (MEMS) gyroscope and to improve the reliability of output data, a filtering algorithm based on orthogonal demodulation is proposed. According to the working principle and failure mechanism of a MEMS gyroscope, the sound and angular velocity frequencies are not identical, which lead to a different frequency signal output of the original single-channel demodulation scheme. Therefore, a Q channel demodulation filtering process was added to the origin single-channel demodulation scheme. For the Q channel demodulated signal, a Hilbert transform was used to compensate for the 90 degree phase shift. The IQ dual-channel difference can remove the acoustic interference signal. The simulation results indicate that the scheme can effectively suppress the acoustic interference signal and it can eliminate more than 95% of the impact of sound waves. We assembled the acoustic interference experimental platform, collected the driving and sensing data, and verified the denoising performance with our algorithm, which eliminated more than 70% of the noise signal. The simulation and experimental results demonstrate that the scheme can eliminate acoustic interference signal without destroying angular velocity signal.

Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.

An Energy-Efficient Fall Detection Method Based on FD-DNN for Elderly People

A fall detection module is an important component of community-based care for the elderly to reduce their health risk. It requires the accuracy of detections as well as maintains energy saving. In order to meet the above requirements, a sensing module-integrated energy-efficient sensor was developed which can sense and cache the data of human activity in sleep mode, and an interrupt-driven algorithm is proposed to transmit the data to a server integrated with ZigBee. Secondly, a deep neural network for fall detection (FD-DNN) running on the server is carefully designed to detect falls accurately. FD-DNN, which combines the convolutional neural networks (CNN) with long short-term memory (LSTM) algorithms, was tested on both with online and offline datasets. The experimental result shows that it takes advantage of CNN and LSTM, and achieved 99.17% fall detection accuracy, while its specificity and sensitivity are 99.94% and 94.09%, respectively. Meanwhile, it has the characteristics of low power consumption.

Cantilever Type Acceleration Sensors Made by Roll-to-Roll Slot-Die Coating

This paper presents the fabrication by means of roll-to-roll slot-die coating and characterization of air gap-based cantilever type capacitive acceleration sensors. As the mass of the sensor moves in the opposite direction of the acceleration, a capacitance change occurs. The sensor is designed to have a six layers structure with an air gap. Fabrication of the air gap and cantilever was enabled by coating and removing water-soluble PVA. The bottom electrode, the dielectric layer, and the sacrificial layer were formed using the roll-to-roll slot-die coating technique. The spacer, the top electrode, and the structural layer were formed by spin coating. Several kinds of experiments were conducted for characterization of the fabricated sensor samples. Experimental results show that accelerations of up to 3.6 g can be sensed with an average sensitivity of 0.00856 %/g.

Co-Creation with Older Adults to Improve User-Experience of a Smartphone Self-Test Application to Assess Balance Function

This co-creation study aimed to develop a smartphone self-test application for balance and leg strength in collaboration between older adults and the research team. The paper describes older participants’ preferences for, and their contribution to, the application design. Technology to assess movements is available in smartphones with built-in sensors, and one of the challenges is to develop a valuable self-test for older adults. The participants contributed to the design of the application’s instructions and user interface. Multiple data collection methods were used: user-test with Think aloud method, mock-ups, homework assignment as co-researcher, audio and video recordings. Qualitative content analysis with a deductive-inductive approach was used, guided by the Optimized Honeycomb model for user experience (UX) as a categorization matrix. The analysis resulted in 17 subcategories within the seven facets of the UX Honeycomb model (findable, accessible, usable, desirable, credible, useful, and valuable), and describes the older participants’ preferences and experiences. The main results were participants’ desire to know why, to get clear and appropriate information, and expectations of the self-test to be useful. It was feasible and valuable to develop the self-test application in co-creation with the intended user-group, in order to get direct feedback and suggestions for the development.

Biosensors for Personal Mobile Health: A System Architecture Perspective

Advances in mobile biosensors, integrating developments in materials science and instrumentation, are fueling an expansion in health data being collected and analyzed in decentralized settings. For example, semiconductor-based sensors are enabling measurement of vital signs, and microfluidic-based sensors are enabling measurement of biochemical markers. As biosensors for mobile health are becoming increasingly paired with smart devices, it will become critical for researchers to design biosensors - with appropriate functionalities and specifications - to work seamlessly with accompanying connected hardware and software. This article describes recent research in biosensors, as well as current mobile health devices in use, as classified into four distinct system architectures that take into account the biosensing and data processing functions required in personal mobile health devices. We also discuss the path forward for integrating biosensors into smartphone-based mobile health devices.

A 2D Non-Linear Second-Order Differential Model for Electrostatic Circular Membrane MEMS Devices: A Result of Existence and Uniqueness

In the framework of 2D circular membrane Micro-Electric-Mechanical-Systems (MEMS), a new non-linear second-order differential model with singularity in the steady-state case is presented in this paper. In particular, starting from the fact that the electric field magnitude is locally proportional to the curvature of the membrane, the problem is formalized in terms of the mean curvature. Then, a result of the existence of at least one solution is achieved. Finally, two different approaches prove that the uniqueness of the solutions is not ensured.

Silicon-Based Sensors for Biomedical Applications: A Review

The paper highlights some of the significant works done in the field of medical and biomedical sensing using silicon-based technology. The use of silicon sensors is one of the pivotal and prolonged techniques employed in a range of healthcare, industrial and environmental applications by virtue of its distinct advantages over other counterparts in Microelectromechanical systems (MEMS) technology. Among them, the sensors for biomedical applications are one of the most significant ones, which not only assist in improving the quality of human life but also help in the field of microfabrication by imparting knowledge about how to develop enhanced multifunctional sensing prototypes. The paper emphasises the use of silicon, in different forms, to fabricate electrodes and substrates for the sensors that are to be used for biomedical sensing. The electrical conductivity and the mechanical flexibility of silicon vary to a large extent depending on its use in developing prototypes. The article also explains some of the bottlenecks that need to be dealt with in the current scenario, along with some possible remedies. Finally, a brief market survey is given to estimate a probable increase in the usage of silicon in developing a variety of biomedical prototypes in the upcoming years.

A MEMS Micro-g Capacitive Accelerometer Based on Through-Silicon-Wafer-Etching Process

This paper presents a micromachined micro-g capacitive accelerometer with a silicon-based spring-mass sensing element. The displacement changes of the proof mass are sensed by an area-variation-based capacitive displacement transducer that is formed by the matching electrodes on both the movable proof mass die and the glass cover plate through the flip-chip packaging. In order to implement a high-performance accelerometer, several technologies are applied: the through-silicon-wafer-etching process is used to increase the weight of proof mass for lower thermal noise, connection beams are used to reduce the cross-sensitivity, and the periodic array area-variation capacitive displacement transducer is applied to increase the displacement-to-capacitance gain. The accelerometer prototype is fabricated and characterized, demonstrating a scale factor of 510 mV/g, a noise floor of 2 µg/Hz^1/2 at 100 Hz, and a bias instability of 4 µg at an averaging time of 1 s. Experimental results suggest that the proposed MEMS capacitive accelerometer is promising to be used for inertial navigation, structural health monitoring, and tilt measurement applications.

Micro-LEGO for MEMS

The recently developed transfer printing-based microassembly called micro-LEGO has been exploited to enable microelectromechanical systems (MEMS) applications which are difficult to achieve using conventional microfabrication. Micro-LEGO involves transfer printing and thermal processing of prefabricated micro/nanoscale materials to assemble structures and devices in a 3D manner without requiring any wet or vacuum processes. Therefore, it complements existing microfabrication and other micro-assembly methods. In this paper, the process components of micro-LEGO, including transfer printing with polymer stamps, material preparation and joining, are summarized. Moreover, recent progress of micro-LEGO within MEMS applications are reviewed by investigating several example devices which are partially or fully assembled via micro-LEGO.

Towards Point-of-Care Insulin Detection

Good glucose management through an insulin dose regime based on the metabolism of glucose helps millions of people worldwide manage their diabetes. Since Banting and Best extracted insulin, glucose management has improved due to the introduction of insulin analogues that act from 30 minutes to 28 days, improved insulin dose regimes, and portable glucose meters, with a current focus on alternative sampling sites that are less invasive. However, a piece of the puzzle is still missing-the ability to measure insulin directly in a Point-of-Care device. The ability to measure both glucose and insulin concurrently will enable better glucose control by providing an improved estimate for insulin sensitivity, minimizing variability in control, and maximizing safety from hypoglycaemia. However, direct detection of free insulin has provided a challenge due to the size of the molecule, the low concentration of insulin in blood, and the selectivity against interferants in blood. This review summarizes current insulin detection methods from immunoassays to analytical chemistry, and sensors. We also discuss the challenges and potential of each of the methods towards Point-of-Care insulin detection.

Prototyping and Validation of MEMS Accelerometers for Structural Health Monitoring—The Case Study of the Pietratagliata Cable-Stayed Bridge

In recent years, thanks to the simple and yet efficient design, Micro Electro-Mechanical Systems (MEMS) accelerometers have proven to offer a suitable solution for Structural Health Monitoring (SHM) in civil engineering applications. Such devices are typically characterised by high portability and durability, as well as limited cost, hence resulting in ideal tools for applications in buildings and infrastructure. In this paper, original self-made MEMS sensor prototypes are presented and validated on the basis of preliminary laboratory tests (shaking table experiments and noise level measurements). Based on the well promising preliminary outcomes, their possible application for the dynamic identification of existing, full-scale structural assemblies is then discussed, giving evidence of their potential via comparative calculations towards past literature results, inclusive of both on-site, Experimental Modal Analysis (EMA) and Finite Element Analytical estimations (FEA). The full-scale experimental validation of MEMS accelerometers, in particular, is performed using, as a case study, the cable-stayed bridge in Pietratagliata (Italy). Dynamic results summarised in the paper demonstrate the high capability of MEMS accelerometers, with evidence of rather stable and reliable predictions, and suggest their feasibility and potential for SHM purposes.

A Wearable System for Real-Time Continuous Monitoring of Physical Activity

Over the last decades, wearable systems have gained interest for monitoring of physiological variables, promoting health, and improving exercise adherence in different populations ranging from elite athletes to patients. In this paper, we present a wearable system for the continuous real-time monitoring of respiratory frequency (f_R), heart rate (HR), and movement cadence during physical activity. The system has been experimentally tested in the laboratory (by simulating the breathing pattern with a mechanical ventilator) and by collecting data from one healthy volunteer. Results show the feasibility of the proposed device for real-time continuous monitoring of f_R, HR, and movement cadence both in resting condition and during activity. Finally, different synchronization techniques have been investigated to enable simultaneous data collection from different wearable modules.

Sockets for Limb Prostheses: A Review of Existing Technologies and Open Challenges

In the prosthetics field, one of the most important bottlenecks is still the human-machine interface, namely the socket. Indeed, a large number of amputees still rejects prostheses or points out a low satisfaction level, due to a sub-optimal interaction between the socket and the residual limb tissues. The aim of this paper is to describe the main parameters (displacements, stress, volume fluctuations and temperature) affecting the stump-socket interface and reducing the comfort/stability of limb prostheses. In this review, a classification of the different socket types proposed in the literature is reported, together with an analysis of advantages and disadvantages of the different solutions, from multiple viewpoints. The paper then describes the technological solutions available to face an altered distribution of stresses on the residual limb tissues, volume fluctuations affecting the stump overtime and temperature variations affecting the residual tissues within the socket. The open challenges in this research field are highlighted and the possible future routes are discussed, towards the ambitious objective of achieving an advanced socket able to self-adapt in real-time to the complex interplay of factors affecting the stump, during both static and dynamic tasks.

[-25]A Similarity Analysis of Audio Signal to Develop a Human Activity Recognition Using Similarity Networks

Human Activity Recognition (HAR) is one of the main subjects of study in the areas of computer vision and machine learning due to the great benefits that can be achieved. Examples of the study areas are: health prevention, security and surveillance, automotive research, and many others. The proposed approaches are carried out using machine learning techniques and present good results. However, it is difficult to observe how the descriptors of human activities are grouped. In order to obtain a better understanding of the the behavior of descriptors, it is important to improve the abilities to recognize the human activities. This paper proposes a novel approach for the HAR based on acoustic data and similarity networks. In this approach, we were able to characterize the sound of the activities and identify those activities looking for similarity in the sound pattern. We evaluated the similarity of the sounds considering mainly two features: the sound location and the materials that were used. As a result, the materials are a good reference classifying the human activities compared with the location.

New digital adherence devices could prevent millions of strokes from atrial fibrillation by the end of the next century

The effectiveness and safety of a pharmacologic intervention is highly dependent on patient's capability to follow the recommended treatment regimen. Non-adherence to pharmacologic treatments is associated with worsening conditions including hospitalization and death. This is a significant burden to healthcare systems on a global scale with non-adherence rates being as high (or higher) as 50% in the first treatment year. The most common causes for non-adherence are forgetfulness, busy lifestyle or complexity and changes in therapeutic schedules. In conditions like atrial fibrillation (AFib) this leads to a drastic increase in event rates, e.g. strokes. Patients diagnosed with AFib are strongly recommended to receive anticoagulant treatments for stroke prevention. Treatments with Vitamin K antagonists or novel oral anticoagulants (NOACs) can dramatically lower the risk of ischemic strokes in the presence of AFib. Non-adherence can expose the patients to an increased stroke risk. This is especially true for NOACs, due to their short half-life. Patients have to take these medications once or twice daily for adequate stroke prevention, i.e., single non-use of the medication can already diminish or reset the anticoagulative effect. Adherence devices could help improve patient's compliance by reminder or feedback function. They have shown to be successful in a number of clinical trails. Especially, newer devices that make use of digital technologies show promising results but are not used broadly in clinical practice. Here we provide evidence for our hypothesis that newly available adherence devices might increase adherence rates and thereby reduce the number of strokes in patients with AFib.

Effective deployment of technology-supported management of chronic respiratory conditions: a call for stakeholder engagement

Background

Healthcare systems are under increasing strain, predominantly due to chronic non-communicable diseases. Connected healthcare technologies are becoming ever more capable and their components cheaper. These innovations could facilitate both self-management and more efficient use of healthcare resources for common respiratory diseases such as asthma and chronic obstructive pulmonary disease. However, newer technologies can only facilitate major changes in practice, and cannot accomplish them in isolation.

Focus of review

There are now large numbers of devices and software offerings available. However, the potential of such technologies is not being realised due to limited engagement with the public, clinicians and providers, and a relative paucity of evidence describing elements of best practice in this complex and evolving environment. Indeed, there are clear examples of wasted resources and potential harm. We therefore call on interested parties to work collaboratively to begin to realize the potential benefits and reduce the risks of connected technologies through change in practice. We highlight key areas where such partnership can facilitate the effective and safe use of technology in chronic respiratory care: developing data standards and fostering inter-operability, making collaborative testing facilities available at scale for small to medium enterprises, developing and promoting new adaptive trial designs, developing robust health economic models, agreeing expedited approval pathways, and detailed planning of dissemination to use.

Conclusion

The increasing capability and availability of connected technologies in respiratory care offers great opportunities and significant risks. A co-ordinated collaborative approach is needed to realize these benefits at scale. Using newer technologies to revolutionize practice relies on widespread engagement and cannot be delivered by a minority of interested specialists. Failure to engage risks a costly and inefficient chapter in respiratory care.

Static and Dynamic Accuracy of an Innovative Miniaturized Wearable Platform for Short Range Distance Measurements for Human Movement Applications

Magneto-inertial measurement units (MIMU) are a suitable solution to assess human motor performance both indoors and outdoors. However, relevant quantities such as step width and base of support, which play an important role in gait stability, cannot be directly measured using MIMU alone. To overcome this limitation, we developed a wearable platform specifically designed for human movement analysis applications, which integrates a MIMU and an Infrared Time-of-Flight proximity sensor (IR-ToF), allowing for the estimate of inter-object distance. We proposed a thorough testing protocol for evaluating the IR-ToF sensor performances under experimental conditions resembling those encountered during gait. In particular, we tested the sensor performance for different (i) target colors; (ii) sensor-target distances (up to 200 mm) and (iii) sensor-target angles of incidence (AoI) (up to 60 ∘ ). Both static and dynamic conditions were analyzed. A pendulum, simulating the oscillation of a human leg, was used to generate highly repeatable oscillations with a maximum angular velocity of 6 rad/s. Results showed that the IR-ToF proximity sensor was not sensitive to variations of both distance and target color (except for black). Conversely, a relationship between error magnitude and AoI values was found. For AoI equal to 0 ∘ , the IR-ToF sensor performed equally well both in static and dynamic acquisitions with a distance mean absolute error <1.5 mm. Errors increased up to 3.6 mm (static) and 11.9 mm (dynamic) for AoI equal to ± 30 ∘ , and up to 7.8 mm (static) and 25.6 mm (dynamic) for AoI equal to ± 60 ∘ . In addition, the wearable platform was used during a preliminary experiment for the estimation of the inter-foot distance on a single healthy subject while walking. In conclusion, the combination of magneto-inertial unit and IR-ToF technology represents a valuable alternative solution in terms of accuracy, sampling frequency, dimension and power consumption, compared to existing technologies.

A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future.

A Reconfigurable Readout Integrated Circuit for Heterogeneous Display-Based Multi-Sensor Systems

This paper presents a reconfigurable multi-sensor interface and its readout integrated circuit (ROIC) for display-based multi-sensor systems, which builds up multi-sensor functions by utilizing touch screen panels. In addition to inherent touch detection, physiological and environmental sensor interfaces are incorporated. The reconfigurable feature is effectively implemented by proposing two basis readout topologies of amplifier-based and oscillator-based circuits. For noise-immune design against various noises from inherent human-touch operations, an alternate-sampling error-correction scheme is proposed and integrated inside the ROIC, achieving a 12-bit resolution of successive approximation register (SAR) of analog-to-digital conversion without additional calibrations. A ROIC prototype that includes the whole proposed functions and data converters was fabricated in a 0.18 μm complementary metal oxide semiconductor (CMOS) process, and its feasibility was experimentally verified to support multiple heterogeneous sensing functions of touch, electrocardiogram, body impedance, and environmental sensors.

Validation and calibration of HeadCount, a self-report measure for quantifying heading exposure in soccer players

The long-term effects of repetitive head impacts due to heading are an area of increasing concern, and exposure must be accurately measured; however, the validity of self-report of cumulative soccer heading is not known. In order to validate HeadCount, a 2-week recall questionnaire, the number of player-reported headers was compared to the number of headers observed by trained raters for a men's and a women's collegiate soccer teams during an entire season of competitive play using Spearman's correlations and intraclass correlation coefficients (ICCs), and calibrated using a generalized estimating equation. The average Spearman's rho was 0.85 for men and 0.79 for women. The average ICC was 0.75 in men and 0.38 in women. The calibration analysis demonstrated that men tend to report heading accurately while women tend to overestimate. HeadCount is a valid instrument for tracking heading behaviour, but may have to be calibrated in women.

Emerging Trends in Healthcare Adoption of Wireless Body Area Networks

Real-time personal health monitoring is gaining new ground with advances in wireless communications. Wireless body area networks (WBANs) provide a means for low-powered sensors, affixed either on the human body or in vivo, to communicate with each other and with external telecommunication networks. The healthcare benefits of WBANs include continuous monitoring of patient vitals, measuring postacute rehabilitation time, and improving quality of medical care provided in medical emergencies. This study sought to examine emerging trends in WBAN adoption in healthcare. To that end, a systematic literature survey was undertaken against the PubMed database. The search criteria focused on peer-reviewed articles that contained the keywords "wireless body area network" and "healthcare" or "wireless body area network" and "health care." A comprehensive review of these articles was performed to identify adoption dimensions, including underlying technology framework, healthcare subdomain, and applicable lessons-learned. This article benefits healthcare technology professionals by identifying gaps in implementation of current technology and highlighting opportunities for improving products and services.

New methods for the assessment of Parkinson's disease (2005 to 2015): A systematic review

BACKGROUND

The past decade has witnessed a highly dynamic and growing expansion of novel methods aimed at improving the assessment of Parkinson's disease with technology (NAM-PD) in laboratory, clinical, and home environments. However, the current state of NAM-PD regarding their maturity, feasibility, and usefulness in assessing the main PD features has not been systematically evaluated.

METHODS

A systematic review of articles published in the field from 2005 to 2015 was performed. Of 9,503 publications identified in PubMed and the Web of Science, 848 full papers were evaluated, and 588 original articles were assessed to evaluate the technological, demographic, clinimetric, and technology transfer readiness parameters of NAM-PD.

RESULTS

Of the studies, 65% included fewer than 30 patients, < 50% employed a standard methodology to validate diagnostic tests, 8% confirmed their results in a different dataset, and 87% occurred in a clinic or lab. The axial features domain was the most frequently studied, followed by bradykinesia. Rigidity and nonmotor domains were rarely investigated. Only 6% of the systems reached a technology level that justified the hope of being included in clinical assessments in a useful time period.

CONCLUSIONS

This systematic evaluation provides an overview of the current options for quantitative assessment of PD and what can be expected in the near future. There is a particular need for standardized and collaborative studies to confirm the results of preliminary initiatives, assess domains that are currently underinvestigated, and better validate the existing and upcoming NAM-PD. © 2016 International Parkinson and Movement Disorder Society.

Macrophages, Foreign Body Giant Cells and Their Response to Implantable Biomaterials

All biomaterials, when implanted in vivo, elicit cellular and tissue responses. These responses include the inflammatory and wound healing responses, foreign body reactions, and fibrous encapsulation of the implanted materials. Macrophages are myeloid immune cells that are tactically situated throughout the tissues, where they ingest and degrade dead cells and foreign materials in addition to orchestrating inflammatory processes. Macrophages and their fused morphologic variants, the multinucleated giant cells, which include the foreign body giant cells (FBGCs) are the dominant early responders to biomaterial implantation and remain at biomaterial-tissue interfaces for the lifetime of the device. An essential aspect of macrophage function in the body is to mediate degradation of bio-resorbable materials including bone through extracellular degradation and phagocytosis. Biomaterial surface properties play a crucial role in modulating the foreign body reaction in the first couple of weeks following implantation. The foreign body reaction may impact biocompatibility of implantation devices and may considerably impact short- and long-term success in tissue engineering and regenerative medicine, necessitating a clear understanding of the foreign body reaction to different implantation materials. The focus of this review article is on the interactions of macrophages and foreign body giant cells with biomaterial surfaces, and the physical, chemical and morphological characteristics of biomaterial surfaces that play a role in regulating the foreign body response. Events in the foreign body response include protein adsorption, adhesion of monocytes/macrophages, fusion to form FBGCs, and the consequent modification of the biomaterial surface. The effect of physico-chemical cues on macrophages is not well known and there is a complex interplay between biomaterial properties and those that result from interactions with the local environment. By having a better understanding of the role of macrophages in the tissue healing processes, especially in events that follow biomaterial implantation, we can design novel biomaterials-based tissue-engineered constructs that elicit a favorable immune response upon implantation and perform for their intended applications.