Human Activity Monitoring Research Articles

Bioinspired Self-Powered Piezoresistive Sensors for Simultaneous Monitoring of Human Health and Outdoor UV Light Intensity.

The exact fabrication of precise three-dimensional structures for piezoresistive sensors necessitates superior manufacturing methods or tooling, which are accompanied by time-consuming processes and the potential for environmental harm. Herein, we demonstrated a method for in situ synthesis of zinc oxide nanorod (ZnO NR) arrays on graphene-treated cotton and paper substrates and constructed highly sensitive, flexible, wearable, and chemically stable strain sensors. Based on the structure of pine trees and needles in nature, the hybrid sensing layer consisted of graphene-attached cotton or paper fibers and ZnO NRs, and the results showed a high sensitivity of 0.389, 0.095, and 0.029 kPa-1 and an ultra-wide linear range of 0-100 kPa of this sensor under optimal conditions. Our study found that water absorption and swelling of graphene fibers and the associated reduction of pore size and growth of zinc oxide were detrimental to pressure sensor performance. A random line model was developed to examine the effects of different hydrothermal times on sensor performance. Meanwhile, pulse detection, respiration detection, speech recognition, and motion detection, including finger movements, walking, and throat movements, were used to show their practical application in human health activity monitoring. In addition, monolithically grown ZnO NRs on graphene cotton sheets had been integrated into a flexible sensing platform for outdoor UV photo-indication, which is, to our knowledge, the first successful case of an integrated UV photo-detector and motion sensor. Due to its excellent strain detection and UV detection abilities, these strategies are a step forward in developing wearable sensors that are cost-controllable and high-performance.

Stretchable freezing-tolerant triboelectric nanogenerator and strain sensor based on transparent, long-term stable, and highly conductive gelatin-based organohydrogel

Conductive hydrogels have attracted tremendous attention for the next-generation electronic/energy/robotic application owing to their excellent mechanical and electrical properties, including stretchability, high conductivity, and stability. How to simultaneously realize their highly transparent, self-healing, antifreezing/antidrying, biocompatible and highly conductive features through a simple approach is still a challenge. Herein, the stretchable freezing-tolerant triboelectric nanogenerator (TENG) and strain sensor have been developed based on a transparent, long-term stable, and highly conductive gelatin/NaCl organohydrogel (GNOH), which is prepared via a facile immersion strategy in a glycerol/water binary solvent. The GNOH demonstrates superior merits of strain (300%), transparency (85%), high conductivity (1.6 S/m), freezing tolerance (−20 ℃), self-healing capability (91%), and environment stability (over 30 days). Furthermore, the wearable strain sensors enable the detecting, quantifying, and monitoring of human activities with high sensitivity of about 2.5 (strain of 0–200%). Meanwhile, the fabricated TENG in single-electrode mode can achieve excellent electrical outputs to power portable electronics when efficiently harvesting biomechanical energies, even at harsh cold temperature (−20 ℃). Additionally, a flexible self-powered calculator based on the arrayed TENG as touch panel is also explored for human-machine interaction (HMI). This study paves the way for potential applications in wearable electronics, healthcare monitoring, biomechanical energy harvesting and HMI.

Daily Living Activity Recognition In-The-Wild: Modeling and Inferring Activity-Aware Human Contexts

Advancement in smart sensing and computing technologies has provided a dynamic opportunity to develop intelligent systems for human activity monitoring and thus assisted living. Consequently, many researchers have put their efforts into implementing sensor-based activity recognition systems. However, recognizing people’s natural behavior and physical activities with diverse contexts is still a challenging problem because human physical activities are often distracted by changes in their surroundings/environments. Therefore, in addition to physical activity recognition, it is also vital to model and infer the user’s context information to realize human-environment interactions in a better way. Therefore, this research paper proposes a new idea for activity recognition in-the-wild, which entails modeling and identifying detailed human contexts (such as human activities, behavioral environments, and phone states) using portable accelerometer sensors. The proposed scheme offers a detailed/fine-grained representation of natural human activities with contexts, which is crucial for modeling human-environment interactions in context-aware applications/systems effectively. The proposed idea is validated using a series of experiments, and it achieved an average balanced accuracy of 89.43%, which proves its effectiveness.

Anthropogenic impacts on phytosociological features and soil microbial health of Colchicum luteum L. an endangered medicinal plant of North Western Himalaya

Colchicum luteum is currently a rare and threatened medicinal plant species in the Kashmir Himalaya. Due to the subsequent increase in anthropogenic pressure on medicinal plant species, it is imperative to understand the phytosociological and conservational status of the plant in its natural habitat. The objectives of this study were analysed in year 2018–2019 on the phytosociological data, viz. density, frequency, and abundance, as well as the rhizospheric soil microbial diversity of C. luteum in disturbed and undisturbed areas of the Kashmir Himalaya. We examined the distribution pattern, phytosociological data, and conservation status of C. luteum by analysing ecological features like abundance, frequency, and density in all three selected locations in Kashmir, Northern India and were found maximum values at Undisturbed areas. The highest values of density (3.24 ± 0.69 m2), frequency (57.77 ± 13.55%), and abundance (5.49 m2) were recorded at undisturbed site Harwan. The total bacterial count (CFU) and Vesicular Arbuscular Mycorrhiza (VAM) spore population from the rhizospheric soil of C. luteum were also analysed, with higher bacterial count i.e., Pseudomonas, Azatobacter, Rhizobium and PSB were (26.2 ± 0.648) (21.88 ± 0.675) (30.11 ± 0.576) and (14.11 ± 0.671) and VAM spore population (g−1) of soil recorded 6.36 ± 0.550 at undisturbed areas viz. Harwan. The bacteria and fungi are likely keystone organisms that form an interface between soils and plant roots. Mutualistic associations with host plants have been observed in various natural and agricultural ecosystems. The present findings could be helpful in formulating conservation strategies for C. Luteum threatened and endangered medicinal plant present in North western Himalayan regions. The plant in disturbed areas that are affected by anthropogenic activities like tourism, grazing, deforestation, urbanization, transport etc. impacts on phytosociological and soil microbial patterns in the area. Because of these abiotic pressures, causes a reduction in plant cover in forest regions, soils become exposed, affecting soil microbial health. Therefore, the study shows the necessity for best practices for medicinal plant and forest management that provide effective monitoring and regulation of human activities in the offshore forest regions and avoid the intrusion of existing reserves.

A Template Matching Based Feature Extraction for Activity Recognition

Human activity recognition (HAR) can play a vital role in the monitoring of human activities, particularly for healthcare conscious individuals. The accuracy of HAR systems is completely reliant on the extraction of prominent features. Existing methods find it very challenging to extract optimal features due to the dynamic nature of activities, thereby reducing recognition performance. In this paper, we propose a robust feature extraction method for HAR systems based on template matching. Essentially, in this method, we want to associate a template of an activity frame or sub-frame comprising the corresponding silhouette. In this regard, the template is placed on the frame pixels to calculate the equivalent number of pixels in the template correspondent those in the frame. This process is replicated for the whole frame, and the pixel is directed to the optimum match. The best count is estimated to be the pixel where the silhouette (provided via the template) presented inside the frame. In this way, the feature vector is generated. After feature vector generation, the hidden Markov model (HMM) has been utilized to label the incoming activity. We utilized different publicly available standard datasets for experiments. The proposed method achieved the best accuracy against existing state-of-the-art systems.

Network crack-based high performance stretchable strain sensors for human activity and healthcare monitoring

In this study, high performance wearable and stretchable strain sensors are developed for human activity and healthcare monitoring, and wearable electronics.

Paper-based sensors for diagnostics, human activity monitoring, food safety and environmental detection

Paper-based sensors exploiting the advantages of paper can replace traditional substrate materials for building sensors which are simple to manufacture, inexpensive, easy-to-operate, portable and disposable.

High-performance pressure/strain sensors featuring a conductive network constructed from c-MWCNTs and nanospheres for human activity monitoring

Human activity monitoring is a complex endeavor, which mainly depends on pressure or strain sensors.

Automatic Unusual Activities Recognition Using Deep Learning in Academia

In the current era, automatic surveillance has become an active research problem due to its vast real-world applications, particularly for maintaining law and order. A continuous manual monitoring of human activities is a tedious task. The use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few years. Various computer vision techniques have been applied for observation and surveillance of real-world activities. This research study focuses on detecting and recognizing unusual activities in an academic situation such as examination halls, which may help the invigilators observe and restrict the students from cheating or using unfair means. To the best of our knowledge, this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual activities. The proposed model has been named Automatic Unusual Activity Recognition (AUAR), which employs motion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional Neural Network algorithm with diverse configurations. The evaluation using standard performance measures confirm that the AUAR model outperforms the already proposed approaches for unusual activity recognition. Apart from evaluating the proposed model on the examination dataset, we also apply AUAR on Violent and Movies datasets, widely used in the relevant literature to detect suspicious activities. The results reveal that AUAR performs well on various data sets compared to existing state-of-the-art models.

A Wearable Electrostatic Sensor for Human Activity Monitoring

Human activity monitoring based on wearable sensors is important in a wide range of biomedical and healthcare applications. Existing wearable sensors using a single unit cannot capture the movements of all body segments. This paper presents a novel wearable electrostatic sensor that can detect limb and torso movements during routine daily activities from any location on the body. Because the electric potential of the human body varies during movements, the sensor measures the potential difference between the body and the electrode for motion sensing. A charge amplifier converts the induced charge on the electrode into a voltage signal, which is further amplified, filtered, digitalized and transmitted via ZigBee. Experimental assessment was carried out by collecting sensor signals from three locations simultaneously while the subject performing different movements. The capability of the sensor to capture limb and torso movements from any location is validated. The characteristics of the sensor are quantified by correlating the sensor signal to simple and cyclic movements. It is found that the sensor signal depends on the sensor’s mounting location on the body, the type of activity and various factors.

Embedded AM-FM Signal Decomposition Algorithm for Continuous Human Activity Monitoring

AM-FM decomposition techniques have been successfully used for extracting significative features from a large variety of signals, helping realtime signal monitoring and pattern recognition, since they represent signals as a simultaneous composition of amplitude modulation and frequency modulation, where the carriers, amplitude envelopes, and the instantaneous frequencies are the features to be estimated. Human activities often involve repetitive movements, such as in running or cycling, where sinusoidal AM-FM decompositions of signals have already demonstrated to be useful to extract compact features to aid monitoring, classification, or detection. In this work we thus present the challenges and results of implementing the iterated coherent Hilbert decomposition (ICHD), a particularly effective algorithm to obtain an AM-FM decomposition, within a resource-constrained and low-power ARM Cortex-M4 microcontroller that is present in a wearable sensor we developed. We apply ICHD to the gyroscope data acquired from an inertial measurement unit (IMU) that is present in the sensor. Optimizing the implementation allowed us to achieve real-time performance using less then 16 % of the available CPU time, while consuming only about 5.4 mW of power, which results in a run-time of over 7 days using a small 250 mAh rechargeable cell.

Smartphone Inertial Sensors Applications in Medical Care Field: A Comprehensive Review

Sensors of various types are built into the majority of current smartphones each with a distinct function that aids the device's performance. Micro-Electro-Mechanical System (MEMS) inertial sensors, such as gyroscopes (gyros) and accelerometers, are built into smartphones. These embedded inertial sensors have improved user experience and accelerated smartphone development and it can be used to detect events or collect information about human activities. Additionally, machine learning algorithms applied to smartphone sensor data could have a wide range of uses in entertainment, health care, finance and other fields. There have been massive financial gains when a smartphone is utilized to provide telemedicine and remote healthcare assistance. This paper provides a brief overview of all the recent work that have been done in the medical care field by utilizing inertial navigation sensors (gyroscopes and accelerometers) whether the inertial sensors were used together or separately. The goal of this review is to outline the most recent trends in four topics. First, we’ll go over fall detection systems released research publications. Second, we'll go over the most recent scholarly papers on human activity monitoring systems that have been published. Third, we’ll review the most recently released research publications on epilepsy detection. Last, we’ll look over the recently released papers on Parkinson’s disease. This work also includes some important applications that use external inertial sensors for comparison purposes.

Investigating the Performance of Wearable Motion Sensors on recognizing falls and daily activities via machine learning

With sensor-based wearable technologies, high precision monitoring and recognition of human physical activities in real time is becoming more critical to support the daily living requirements of the elderly. The use of sensor technologies, including accelerometers (A), gyroscopes (G), and magnetometers (M) is mostly encountered in work focused on assistive technology, ambient intelligence, context-aware systems, gait and motion analysis, sports science, and fall detection. The classification performance of four sensor type combinations is investigated through the use of four machine learning algorithms: support vector machines (SVMs), Manhattan k-nearest neighbor classifier (M.k-NN), subspace linear discriminant analysis (SLDA), and ensemble bagged decision tree (EBDT). In this context, a large dataset containing 2520 tests performed by 14 volunteers containing 16 activities of daily living (ADLs) and 20 falls was employed. In binary (fall vs. ADL) and multi-class activity (36 activities) recognition, the highest classification accuracy rate was obtained by the SVM (99.96%) and M.k-NN (95.27%) classifiers, respectively, with the AM sensor type combination in both cases. We also made our dataset publicly available to lay the groundwork for new research.

Construction of sensitive strain sensing nanofibrous membrane with polydopamine-modified MXene/CNT dual conductive network

The flexible mechanical sensors that can detect body parameters in real time have been applied in many fields of soft robots, human activity monitoring and health diagnosis. High sensitivity and durable stably signal output are two key factors in strain sensors. The MXene-based materials received extensive attention recently owing to their high conductivity and sense performance, however, poor inherent oxidation resistance properties and weak binding force of MXene-based composite materials always hinder the signal stability. Herein, we propose a facile strategy to prepare polydopamine-modified MXene nanosheets (PMXene) with good oxidation resistance through in situ polymerization, and then it was uniformly loaded on thermoplastic polyurethanes-carbon nanotube (TPU-CNT) nanofibers. Due to the adhesion and interaction between PMXene, CNTs and doped Ni ion, an effective 1D/2D double conductive network is realized in as-prepared flexible sensors. The resultant sensor shows high sensitivity of 9.25, a tunable detection range at 100% strain, and reliable signal ability over 900 cycles. Furthermore, the adhesive dopamine layer effectively isolates oxygen and ensures the stability of the devices. Notably, the sensor can be employed to recognize various human joint motions and detect the physiological signal of swallowing and vocalization, exhibiting the promising potential of the strain sensor in wearable electronics.

Highly Responsive Asymmetric Pressure Sensor Based on MXene/Reduced Graphene Oxide Nanocomposite Fabricated by Laser Scribing Technique

Flexible pressure sensors play a vital role in wearable devices, human physiological activity monitoring, smart robots and other fields. However, current flexible piezoresistive sensors typically have the disadvantages of complex preparation processes, inability to take into account sensitivity and pressure response range, and poor cycle stability. Herein, a novel flexible asymmetric pressure sensor has been proposed by adjusting sensing material and optimizing the structure of electrode. As a crucial piezoresistive material, MXene/reduced graphene oxide (MXene/rGO) nanocomposite with a layer by layer stacked structure is developed by an efficient and precise one-step laser scribing technology. Benefit from this designed layer by layer crosslinked structure, the proposed flexible MXene/rGO asymmetric pressure sensor exhibits excellent pressure-sensing performances, such as high sensitivity of 2.25 kPa^&#x2212;1 (0 &#x2013; 200 Pa), low detection limit of about 2.5 Pa, fast responsive recovery, and long cycle durability. So that our flexible MXene/rGO asymmetric pressure sensor can detect subtle body physiological motions in real time, such as wrist pulse, vocal vibration, finger movement, and breath. Additionally, the as-prepared sensor array integrated by <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> identical MXene/rGO asymmetric pressure sensors exhibits good multi-touch function. Therefore, such conspicuous sensing performances endow our MXene/rGO asymmetric pressure sensor for widespread practical applications in future flexible wearable electronics.

Highly stretchable liquid-metal based strain sensor with high sensitivity for human activity monitoring

Sensors with high sensitivity, a wide detection range, large stretchability and skin-like elastic compliance are highly desirable for biomedical applications. Liquid metal (LM)-based composites, characterized by being highly stretchable and mechanically compatible to biological tissues, have great potential for stretchable wearable sensors, but they are limited to relatively small resistive changes with the applied stretch. Here we report a highly stretchable, conformable and highly sensitive strain sensor fabricated by inserting putty-like conductive mixture composed of SiO2 microspheres and LM into elastomer microchannels. The introduction of the SiO2 microspheres effectively improves the sensitivity of the sensor while retaining the desirable mechanical properties. The capture of various human activities, such as different states of mouth, finger, hand and elbow, has been demonstrated by using the prepared sensor and a wireless sensory system.

A tough hydrogel with fast self-healing and adhesive performance for wearable sensors

The purpose of this work is to address the two existing challenges. One is to achieve the balance of mechanical properties, adhesion, and self-healing ability in hydrogel used in wearable devices. The other is to use mature products as raw materials and simplify the preparation process of adhesive hydrogels, so as to make it possible for large-scale industrial production. A novel PAA/Fe/PVP hydrogel has been prepared by a simple one-step radical polymerization to meet the above requirements. As the main component of adhesives in daily life, PVP is used to improve the adhesion of the hydrogel. The adhesion as high as 64 kPa is achieved in the hydrogel. The adhesion can also withstand multiple uses due to the good mechanical properties of the hydrogel. Furthermore, the hydrogel displays outstanding self-healing ability in terms of both mechanical properties and conductivity. Because of the good mechanical properties, robust adhesion and rapid self-healing ability, the hydrogel-based sensors have demonstrated stability, accuracy and reliability in real-time monitoring of subtle facial micro-expressions and large human activities. In addition, the method uses mature products as raw materials, and the preparation process is simple, so it has the potential for large-area and low-cost production.

Biosensor Devices in the Production of Alcoholic and Non-Alcoholic Beverages

"Smart" multisensors and biosensor systems based on modern information and communication technologies make it possible to qualitatively improve the parameters of testing systems for biologically active, chemical and toxic substances and biological or biophysical objects, improve parameter control, data processing and analysis in digital agriculture, food industry, environmental monitoring and other areas of human activity. These next-generation devices combine biologically sensitive elements with converters of biophysical signals into electrical digital signals. The article reveals the basic principles of construction of biosensor devices, their practical implementation and application. The own results of development of a wireless network of "smart" multisensors and biosensor devices for express diagnostics of a condition of grape and fruit crops and control of process of production of wine are presented. In order to test the capabilities of the unit of measurement, a number of experimental works were performed. To perform such work, it was first necessary to develop a new embedded software for the microprocessor of Analog Devices ADuCM350, and the corresponding user software for the OS Windows 10. Experiments were performed using disposable sensors based on the enzyme glucose oxidase to measure the sugar content in glucose and wine solution. A review and analysis of modern biosensor devices used in the production of alcoholic and Non-Alcoholic Beverages were done. The comparative table of analyzers for different studies based on biosensors is made. Development and preparation for mass production of "smart" biosensors, biosensor devices and networks based on them is in line with global scientific and technological trends of today and, of course, the near future. Keywords: biosensors, ammetric transducers, wireless sensor network, express diagnostics of grape and berry crops.

Indoor Activity and Vital Sign Monitoring for Moving People with Multiple Radar Data Fusion

The monitoring of human activity and vital signs plays a significant role in remote health-care. Radar provides a non-contact monitoring approach without privacy and illumination concerns. However, multiple people in a narrow indoor environment bring dense multipaths for activity monitoring, and the received vital sign signals are heavily distorted with body movements. This paper proposes a framework based on Frequency Modulated Continuous Wave (FMCW) and Impulse Radio Ultra-Wideband (IR-UWB) radars to address these challenges, designing intelligent spatial-temporal information fusion for activity and vital sign monitoring. First, a local binary pattern (LBP) and energy features are extracted from FMCW radar, combined with the wavelet packet transform (WPT) features on IR-UWB radar for activity monitoring. Then the additional information guided fusing network (A-FuseNet) is proposed with a modified generative and adversarial structure for vital sign monitoring. A Cascaded Convolutional Neural Network (CCNN) module and a Long Short Term Memory (LSTM) module are designed as the fusion sub-network for vital sign information extraction and multisensory data fusion, while a discrimination sub-network is constructed to optimize the fused heartbeat signal. In addition, the activity and movement characteristics are introduced as additional information to guide the fusion and optimization. A multi-radar dataset with an FMCW and two IR-UWB radars in a cotton tent, a small room and a wide lobby is constructed, and the accuracies of activity and vital sign monitoring achieve 99.9% and 92.3% respectively. Experimental results demonstrate the superiority and robustness of the proposed framework.

Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

Human activity monitoring is essential for a variety of applications in many fields, particularly healthcare. The goal of this research work is to develop a system that can effectively detect fall/collapse and classify other discrete daily living activities such as sitting, standing, walking, drinking, and bending. For this paper, a publicly accessible dataset is employed, which is captured at various geographical locations using a 5.8 GHz Frequency-Modulated Continuous-Wave (FMCW) RADAR. A total of ninety-nine participants, including young and elderly individuals, took part in the experimental campaign. During data acquisition, each aforementioned activity was recorded for 5–10 s. Through the obtained data, we generated the micro-doppler signatures using short-time Fourier transform by exploiting MATLAB tools. Subsequently, the micro-doppler signatures are validated, trained, and tested using a state-of-the-art deep learning algorithm called Residual Neural Network or ResNet. The ResNet classifier is developed in Python, which is utilised to classify six distinct human activities in this study. Furthermore, the metrics used to analyse the trained model’s performance are precision, recall, F1-score, classification accuracy, and confusion matrix. To test the resilience of the proposed method, two separate experiments are carried out. The trained ResNet models are put to the test by subject-independent scenarios and unseen data of the above-mentioned human activities at diverse geographical spaces. The experimental results showed that ResNet detected the falling and rest of the daily living human activities with decent accuracy.