Gesture recognition for human-robot collaboration: A review

This review collates around 100 papers that developed micro-electro-mechanical system (MEMS) capacitive microphones. As far as we know, this is the first comprehensive archive from academia on this versatile device from 1989 to 2019. These works are tabulated in term of intended application, fabrication method, material, dimension, and performances. This is followed by discussions on diaphragm, backplate and chamber, and performance parameters. This review is beneficial for those who are interested with the evolutions of this acoustic sensor.

A Review of MEMS Capacitive Microphones

This paper introduces a human posture tracking platform to identify the human postures of sitting, standing or lying down, based on a smartwatch. This work develops such a system as a proof-of-concept study to investigate a smartwatch's ability to be used in future remote health monitoring systems and applications. This work validates the smartwatches' ability to track the posture of users accurately in a laboratory setting while reducing the sampling rate to potentially improve battery life, the first steps in verifying that such a system would work in future clinical settings. The algorithm developed classifies the transitions between three posture states of sitting, standing and lying down, by identifying these transition movements, as well as other movements that might be mistaken for these transitions. The system is trained and developed on a Samsung Galaxy Gear smartwatch, and the algorithm was validated through a leave-one-subject-out cross-validation of 20 subjects. The system can identify the appropriate transitions at only 10 Hz with an F-score of 0.930, indicating its ability to effectively replace smart phones, if needed.

/pdf/can-smartwatches-replace-smartphones-for-posture-tracking-1f5adjr9ox.pdf

Can Smartwatches Replace Smartphones for Posture Tracking

As the availability and use of wearables increases, they are becoming a promising platform for context sensing and context analysis. Smartwatches are a particularly interesting platform for this purpose, as they offer salient advantages, such as their proximity to the human body. However, they also have limitations associated with their small form factor, such as processing power and battery life, which makes it difficult to simply transfer smartphone-based context sensing and prediction models to smartwatches. In this paper, we introduce an energy-efficient, generic, integrated framework for continuous context sensing and prediction on smartwatches. Our work extends previous approaches for context sensing and prediction on wrist-mounted wearables that perform predictive analytics outside the device. We offer a generic sensing module and a novel energy-efficient, on-device prediction module that is based on a semantic abstraction approach to convert sensor data into meaningful information objects, similar to human perception of a behavior. Through six evaluations, we analyze the energy efficiency of our framework modules, identify the optimal file structure for data access and demonstrate an increase in accuracy of prediction through our semantic abstraction method. The proposed framework is hardware independent and can serve as a reference model for implementing context sensing and prediction on small wearable devices beyond smartwatches, such as body-mounted cameras.

/pdf/energy-efficient-integration-of-continuous-context-sensing-1wim6ppnto.pdf

Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches

Human emotions are essential to recognize the behavior and state of mind of a person. Emotion detection through speech signals has started to receive more attention lately. This paper proposes the method for detecting human emotions using speech signals and its implementation in real-time using the Internet of Things (IoT) based deep learning for the care of older adults in nursing homes. The research has two main contributions. First, we have implemented a real-time system based on audio IoT, where we have recorded human voice and predicted emotions via deep learning. Secondly, for advance classification, we have designed a model using data normalization and data augmentation techniques. Finally, we have created an integrated deep learning model, called Speech Emotion Detection (SED), using a 2D convolutional neural networks (CNN). The best accuracy that was reported by our method was approximately 95%, which outperformed all state-of-the-art approaches. We have further extended to apply the SED model to a live audio sentiment analysis system with IoT technologies for the care of older adults in nursing homes.

Speech Emotion Detection using IoT based Deep Learning for Health Care

https://cyberleninka.org/article/n/1122354.pdf

Modelling and Simulation of Cost Effective Sensorless Drive for Brushless DC Motor

In this paper, MEMS-based capacitive microphone and low-cost amplifier are designed for low-cost power-efficient hearing aid application. The developed microphone along with the associated circuitry is mounted on a common board in the form of pocket-type (body-worn) device.
 The designed microphone consists of a flexible circular silicon nitrite (Si3N4) diaphragm and a polysilicon-perforated back plate with air as dielectric between them. The incident acoustic waves on the sensor cause deflection of the diaphragm to alter the air gap between the perforated back plate (fixed electrode) and the diaphragm (moving plate) which causes a change in capacitance. The acoustic pressure applied to the microphone is from 0 to 100 Pa for an operating range of 100 Hz–10 kHz which corresponds to the audible frequency range in case of human beings. The main purpose of this work is to increase the longevity of battery used in conventional hearing aids. The designed MEMS microphone with Si3N4 diaphragm is capable of identifying acoustic frequencies (100 Hz to 10 kHz) which correspond to a specific change in absolute pressure from 0 to 100 Pa for 2-micron-thick diaphragm with a sensitivity of about 0.08676 mV/Pa. The design of the sensor and the characteristics analysis are performed in FEM-based simulation software, which are later validated in real time. The prototype is designed using MEMS microphone and low-cost amplifier ICs with biasing components in the form of pocket-type (body-worn) hearing aid. In order to study the performance of proposed device, three different market-available amplifiers with controllable gain are used. Finally, the performance of the hearing aid is studied through audio spectrogram analysis to choose the best-suited amplifier among the three.

Development and performance analysis of a low-cost MEMS microphone-based hearing aid with three different audio amplifiers

This article presents development of a microcapacitive sensor-based bio-impedance classification system for identification of malignant cells in a known volume (400 $\mu \text{L}$ ) of biological tissue sample Malignancy induces various physical changes in an affected cell, among which increased intracellular water and sodium ion content are a couple of prominent ones In this work, the change in electrical properties of a cell [here white blood cell (WBC)] that is membrane and cytoplasm permittivity due to afore-mentioned reasons is used for distinguishing malignant from normal cells based on their deviation in bio-impedance signature This change in bio-impedance signature of the WBC is measured by exciting the mentioned microcapacitive sensor by a 2 V p-p signal The developed system consists of an on-board impedance analyzer to carry out bio-impedance spectroscopy over a frequency range of 1–100 kHz The bio-impedance data are then obtained from the sensor for different concentrations of cultured human malignant and normal white blood cells The difference in impedance values between concentrations is then used as a discerning factor for identification the degree of malignancy, for which an artificial neural network (ANN) is employed A detailed study considering detection efficiency for this ANN revealed classification accuracy up to 950%, upon which this ANN model is transferred to a portable single board computer (SBC) along with other associated circuitry that could carry out the same classification at a nominal power consumption of 51645 mW

Study and Classification of Cell Bio-Impedance Signature for Identification of Malignancy Using Artificial Neural Network

In this paper, a diaphragm based MEMS capacitive pressure sensor array in conjunction with signal conditioning circuitry is designed for measuring Heart Rate (HR) of human being. In order to measure the heart rate, an array of capacitive sensors are mounted on wrist in such a way that the radial artery blood flow during contraction and expansion of the heart, impinges on the sensor diaphragms. Due to blood flow in the wrist artery, the designed circular diaphragm of the sensor is being deflected and thus induces a corresponding change in the original capacitance value. 
A proposed schematic of the system to measure the change in capacitance consists of signal conditioning circuitry along with the sensors. The structure of the sensor consists of parallel plates having 1.5 micron air gap in between. This change in capacitance is detected by a CMOS based capacitance-to-voltage converter which is designed as a precision interface with the sensor array. 
The designed MEMS sensor has a Poly Silicon diaphragm and is capable of identifying HR (60–100 bpm) which corresponds to a specific change in absolute pressure from 10 to 100 kPa for 5 micron thick diaphragm with 3600 µm area. The design of the sensor along with its fabrication steps and characteristics analysis are performed in FEM based simulation software.

Design and performance analysis of MEMS capacitive pressure sensor array for measurement of heart rate

This paper describes a new mathematical model of permanent magnet brushless dc motor (PMBLDC) with sensorless commutated drive on MATLAB/Simulink based platform. The main challenges in sensorless BLDC commutation techniques compared with sensored drive lies in identification of first commutation point and minimization of torque ripple. In this work, an ideal back-EMF generation methodology has been designed and implemented in order to incline the system with the ideal one. As a result, a significant reduction in torque ripple is achieved. The characteristics of this model give satisfactory outputs over a wide range of controlled speed variation from 700 to 14000 rpm. Moreover, the torque and speed characteristic provide a high torque even at low speed. The strength of the sensorless commutation technique used here is established by performance analysis of the simulated design.

Debjyoti Chowdhury

Papers

Modelling and Simulation of Cost Effective Sensorless Drive for Brushless DC Motor

Development and performance analysis of a low-cost MEMS microphone-based hearing aid with three different audio amplifiers

Study and Classification of Cell Bio-Impedance Signature for Identification of Malignancy Using Artificial Neural Network

Design and performance analysis of MEMS capacitive pressure sensor array for measurement of heart rate

A new approach of sensorless control methodology for achieving ideal characteristics of brushless DC motor using MATLAB/Simulink