Miniaturized wireless, skin-integrated sensor networks for quantifying full-body movement behaviors and vital signs in infants

Early identification of atypical infant movement behaviors consistent with underlying neuromotor pathologies can expedite timely enrollment in therapeutic interventions that exploit inherent neuroplasticity to promote recovery. Traditional neuromotor assessments rely on qualitative evaluations performed by specially trained personnel, mostly available in tertiary medical centers or specialized facilities. Such approaches are high in cost, require geographic proximity to advanced healthcare resources, and yield mostly qualitative insight. This paper introduces a simple, low-cost alternative in the form of a technology customized for quantitatively capturing continuous, full-body kinematics of infants during free living conditions at home or in clinical settings while simultaneously recording essential vital signs data. The system consists of a wireless network of small, flexible inertial sensors placed at strategic locations across the body and operated in a wide-bandwidth and time-synchronized fashion. The data serve as the basis for reconstructing three-dimensional motions in avatar form without the need for video recordings and associated privacy concerns, for remote visual assessments by experts. These quantitative measurements can also be presented in graphical format and analyzed with machine-learning techniques, with potential to automate and systematize traditional motor assessments. Clinical implementations with infants at low and at elevated risks for atypical neuromotor development illustrates application of this system in quantitative and semiquantitative assessments of patterns of gross motor skills, along with body temperature, heart rate, and respiratory rate, from long-term and follow-up measurements over a 3-mo period following birth. The engineering aspects are compatible for scaled deployment, with the potential to improve health outcomes for children worldwide via early, pragmatic detection methods.

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Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.17.3dmp-a17 ◽

2020 ◽

Vol 2020 (17) ◽

pp. 2-1-2-6

Author(s):

Shih-Wei Sun ◽

Ting-Chen Mou ◽

Pao-Chi Chang

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Network Structure ◽

Inertial Sensors ◽

Body Movement ◽

The Body ◽

Body Parts ◽

Multimodal Sensing ◽

Multiple Devices ◽

Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

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Non-intrusive vital sign monitoring using an intelligent pillow based on a piezoelectric ceramic sensor

Journal of Engineered Fibers and Fabrics ◽

10.1177/1558925020977268 ◽

2020 ◽

Vol 15 ◽

pp. 155892502097726

Author(s):

Wei Wang ◽

Zhiqiang Pang ◽

Ling Peng ◽

Fei Hu

Keyword(s):

Heart Rate ◽

Real Time ◽

Piezoelectric Ceramic ◽

Low Cost ◽

Body Movement ◽

Vital Signs ◽

High Heart Rate ◽

Detection Accuracy ◽

Processing Unit ◽

Real Time Monitoring

Performing real-time monitoring for human vital signs during sleep at home is of vital importance to achieve timely detection and rescue. However, the existing smart equipment for monitoring human vital signs suffers the drawbacks of high complexity, high cost, and intrusiveness, or low accuracy. Thus, it is of great need to develop a simplified, nonintrusive, comfortable and low cost real-time monitoring system during sleep. In this study, a novel intelligent pillow was developed based on a low-cost piezoelectric ceramic sensor. It was manufactured by locating a smart system (consisting of a sensing unit i.e. a piezoelectric ceramic sensor, a data processing unit and a GPRS communication module) in the cavity of the pillow made of shape memory foam. The sampling frequency of the intelligent pillow was set at 1000 Hz to capture the signals more accurately, and vital signs including heart rate, respiratory rate and body movement were derived through series of well established algorithms, which were sent to the user’s app. Validation experimental results demonstrate that high heart-rate detection accuracy (i.e. 99.18%) was achieved in using the intelligent pillow. Besides, human tests were conducted by detecting vital signs of six elder participants at their home, and results showed that the detected vital signs may well predicate their health conditions. In addition, no contact discomfort was reported by the participants. With further studies in terms of validity of the intelligent pillow and large-scale human trials, the proposed intelligent pillow was expected to play an important role in daily sleep monitoring.

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Wearable Magnetic Induction-based Approach Toward 3D Motion Tracking

10.21203/rs.3.rs-441596/v1 ◽

2021 ◽

Author(s):

Negar Golestani ◽

Mahta Moghaddam

Keyword(s):

Magnetic Induction ◽

Motion Tracking ◽

Mean Squared Error ◽

Average Distance ◽

Tracking System ◽

Body Movement ◽

The Body ◽

Machine Learning Techniques ◽

Wide Range ◽

Battery Capacity

Abstract Activity recognition using wearable sensors has gained popularity due to its wide range of applications, including healthcare, rehabilitation, sports, and senior monitoring. Tracking the body movement in 3D space facilitates behavior recognition in different scenarios. Wearable systems have limited battery capacity, and many critical challenges have to be addressed to gain a trade-off among power consumption, computational complexity, minimizing the effects of environmental interference, and achieving higher tracking accuracy. This work presents a motion tracking system based on magnetic induction (MI) to tackle the challenges and limitations inherent in designing a wireless monitoring system. We integrated a realistic prototype of an MI sensor with machine learning techniques and investigated one-sensor and two-sensor configuration setups for motion reconstruction. This approach is successfully evaluated using measured and synthesized datasets generated by the analytical model of the MI system. The system has an average distance root-mean-squared error (RMSE) error of 3 cm compared to the ground-truth real-world measured data with Kinect.

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Research on Data Fusion Technology of Body Posture Detection Based on Kalman Filter

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.668-669.1003 ◽

2014 ◽

Vol 668-669 ◽

pp. 1003-1006 ◽

Cited By ~ 1

Author(s):

Xian Wei Wang ◽

Fu Cheng Cao

Keyword(s):

Kalman Filter ◽

Attitude Determination ◽

Inertial Sensors ◽

Low Cost ◽

Micro Electro Mechanical System ◽

Angle Measurement ◽

The Body ◽

Body Posture ◽

Posture Estimation ◽

The Impact

This paper discusses the body posture detection problem using low cost Micro-Electro-Mechanical System (MEMS) inertial sensors, for which a complementary sensor fusion solution is proposed. Considering the impact from the noise and bias drifts, through Kalman filter to complete the multi-sensor information fusion, achieved an accurate attitude determination. The experimental results show that, after using Kalman filtering algorithm to fuse acceleration sensor and signal gyroscope, it can effectively eliminate the accumulative error and significantly better dynamic characteristics of attitude angle measurement, Improving the reliability and accuracy of body posture estimation.

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Identifying human body states by using a flexible integrated sensor

npj Flexible Electronics ◽

10.1038/s41528-020-00090-9 ◽

2020 ◽

Vol 4 (1) ◽

Cited By ~ 1

Author(s):

Ying Jin ◽

Guoning Chen ◽

Kete Lao ◽

Songhui Li ◽

Yong Lu ◽

...

Keyword(s):

Body Temperature ◽

Respiratory Rate ◽

Human Body ◽

Low Cost ◽

Vital Signs ◽

Force Sensor ◽

Diagnostic Methods ◽

Body Parts ◽

Integrated Sensor ◽

Flexible Sensors

Abstract Flexible sensors are required to be lightweight, compatible with the skin, sufficiently sensitive, and easily integrated to extract various kinds of body vital signs during continuous healthcare monitoring in daily life. For this, a simple and low-cost flexible temperature and force sensor that uses only two carbon fiber beams as the sensing layer is reported in this work. This simple, flexible sensor can not only monitor skin temperature changes in real time but can also extract most pulse waves, including venous waves, from most parts of the human body. A pulse diagnostic glove containing three such flexible sensors was designed to simulate pulse diagnostic methods used in traditional Chinese medicine. Wearable equipment was also designed in which four flexible sensors were fixed onto different body parts (neck, chest, armpit, and fingertip) to simultaneously monitor body temperature, carotid pulse, fingertip artery pulse, and respiratory rate. Four important physiological indicators—body temperature (BT), blood pressure (BP), heart rate (HR), and respiratory rate (RR)—were extracted by the wearable equipment and analyzed to identify exercise, excited, tired, angry, and frightened body states.

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Machine-Learning Based Determination of Gait Events from Foot-Mounted Inertial Units

Sensors ◽

10.3390/s21030839 ◽

2021 ◽

Vol 21 (3) ◽

pp. 839

Author(s):

Matteo Zago ◽

Marco Tarabini ◽

Martina Delfino Spiga ◽

Cristina Ferrario ◽

Filippo Bertozzi ◽

...

Keyword(s):

Machine Learning ◽

Inertial Sensors ◽

Time Window ◽

Reaction Force ◽

Deterministic Algorithm ◽

The Body ◽

Machine Learning Techniques ◽

Average Error ◽

Time Duration ◽

Force System

A promising but still scarcely explored strategy for the estimation of gait parameters based on inertial sensors involves the adoption of machine learning techniques. However, existing approaches are reliable only for specific conditions, inertial measurements unit (IMU) placement on the body, protocols, or when combined with additional devices. In this paper, we tested an alternative gait-events estimation approach which is fully data-driven and does not rely on a priori models or assumptions. High-frequency (512 Hz) data from a commercial inertial unit were recorded during 500 steps performed by 40 healthy participants. Sensors’ readings were synchronized with a reference ground reaction force system to determine initial/terminal contacts. Then, we extracted a set of features from windowed data labeled according to the reference. Two gray-box approaches were evaluated: (1) classifiers (decision trees) returning the presence of a gait event in each time window and (2) a classifier discriminating between stance and swing phases. Both outputs were submitted to a deterministic algorithm correcting spurious clusters of predictions. The stance vs. swing approach estimated the stride time duration with an average error lower than 20 ms and confidence bounds between ±50 ms. These figures are suitable to detect clinically meaningful differences across different populations.

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Stretchable Composite Acoustic Transducer for Wearable Monitoring of Vital Signs

10.1101/2019.12.14.876409 ◽

2019 ◽

Author(s):

Yasin Cotur ◽

Michael Kasimatis ◽

Matti Kaisti ◽

Selin Olenik ◽

Charis Georgiou ◽

...

Keyword(s):

High Performance ◽

Low Cost ◽

Vital Signs ◽

The Body ◽

Sound Waves ◽

Acoustic Transducers ◽

Acoustic Transducer ◽

Silicone Polymers ◽

Initial Results ◽

Soft Composite

AbstractWe report a highly flexible, stretchable, and mechanically robust low-cost soft composite consisting of silicone polymers and water (or hydrogels). When combined with conventional acoustic transducers, the materials reported enable high performance real-time monitoring of heart and respiratory patterns over layers of clothing (or furry skin of animals) without the need for direct contact with the skin. Our approach enables an entirely new method of fabrication that involves encapsulation of water and hydrogels with silicones and exploits the ability of sound waves to travel through the body. The system proposed outperforms commercial, metal-based stethoscopes for the auscultation of the heart when worn over clothing and is less susceptible to motion artefacts. We have tested the system both with human and furry animal subjects (i.e. dogs), primarily focusing on monitoring the heart, however, we also present initial results on monitoring breathing. Our work is especially important because it is the first demonstration of a stretchable sensor that is suitable for use with furry animals and do not require shaving of the animal for data acquisition.

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Lora-Based System for Tracking Runners in Cross-Country Races

Proceedings ◽

10.3390/ecsa-6-06629 ◽

2019 ◽

Vol 42 (1) ◽

pp. 32

Author(s):

Sandra Sendra ◽

Pablo Romero-Díaz ◽

José Luis García-Navas ◽

Jaime Lloret

Keyword(s):

Network Architecture ◽

Electronic Device ◽

Low Cost ◽

Vital Signs ◽

The Body ◽

Area Network ◽

Wide Area Network ◽

Cross Country ◽

Pass Through ◽

The Times

In recent years, the organization of cross-country and popular races where hundreds of people participate has become a significant trend. In these events, runners usually subject the body to extreme situations that can lead to various types of indisposition, and they can also suffer falls. Currently, the electronic systems used in this type of race only monitor when runners pass through checkpoints. However, it is necessary to implement systems that enable the control of the population of runners and the monitoring of their status all the times. For this reason, this paper proposes the design of a low-cost system for monitoring and controlling runners in this type of event. The system is formed by a network architecture in infrastructure mode based on low-power wide-area network (LPWAN) technology. Each runner will carry an electronic device that will allow their position and vital signs to be monitored. Likewise, it will incorporate an S.O.S. button that will allow runners to send a signal to the organization should they require help. All these data will be sent through the network to a database, which will allow the organization and bystanders of the race to check the location and history of vital signs of runners. This paper shows the proposal of a design of our system and the different practical experiments that have been carried out with the devices that have allowed for the proposition of this design.

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More music, more health!

Journal of Public Health ◽

10.1093/pubmed/fdaa123 ◽

2020 ◽

Author(s):

Alfredo Raglio

Keyword(s):

New Technologies ◽

Therapeutic Potential ◽

Vital Signs ◽

Clinical Work ◽

Well Being ◽

Therapeutic Interventions ◽

Machine Learning Techniques ◽

Music Listening ◽

Learning Techniques ◽

Therapy Interventions

Abstract For several decades, music has been used more and more frequently and consciously as a mean of care to reduce or stabilize symptoms and/or complications arising therefrom. This has been the case with several diseases and conditions. Indeed, music also gives pleasure, promotes well-being, facilitates the expression and regulation of emotions and improves communication and relationships between individuals. The basis underlying the therapeutic potential of music are to be considered in relation to the extensive action that music itself exerts on the brain but also on vital signs and neurochemical systems. Music therapy interventions are based on active/receptive approaches (characterized by a relational or rehabilitative component) but also on music listening. Music-based interventions can be considered activities aimed at increasing the person's well-being. The objectives of making/listening to music are to improve the person's mood and motivation, promote socialization and stimulate sensory, motor and cognitive aspects. In particular, music listening effects concern structured symptoms and general well-being reducing anxiety and stress. New technologies, such as algorithmic music and machine learning techniques, can also help to develop therapeutic interventions with music and to bring art and science closer together, in the service of medicine, in clinical work and in research.

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The Current Status and Challenges in the Development of Vaccines and Drugs against Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2)

BioMed Research International ◽

10.1155/2021/8160860 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Narasimha M. Beeraka ◽

SubbaRao V. Tulimilli ◽

Medha Karnik ◽

Surya P. Sadhu ◽

Rajeswara Rao Pragada ◽

...

Keyword(s):

Severe Acute Respiratory Syndrome ◽

Low Cost ◽

Clinical Manifestations ◽

Therapeutic Interventions ◽

Detection Methods ◽

Current Status ◽

Eosinophil Infiltration ◽

Effective Vaccine ◽

Serological Tests ◽

Cell Immunity

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection causes coronavirus disease-19 (COVID-19), which is characterized by clinical manifestations such as pneumonia, lymphopenia, severe acute respiratory distress, and cytokine storm. S glycoprotein of SARS-CoV-2 binds to angiotensin-converting enzyme II (ACE-II) to enter into the lungs through membrane proteases consequently inflicting the extensive viral load through rapid replication mechanisms. Despite several research efforts, challenges in COVID-19 management still persist at various levels that include (a) availability of a low cost and rapid self-screening test, (b) lack of an effective vaccine which works against multiple variants of SARS-CoV-2, and (c) lack of a potent drug that can reduce the complications of COVID-19. The development of vaccines against SARS-CoV-2 is a complicated process due to the emergence of mutant variants with greater virulence and their ability to invoke intricate lung pathophysiology. Moreover, the lack of a thorough understanding about the virus transmission mechanisms and complete pathogenesis of SARS-CoV-2 is making it hard for medical scientists to develop a better strategy to prevent the spread of the virus and design a clinically viable vaccine to protect individuals from being infected. A recent report has tested the hypothesis of T cell immunity and found effective when compared to the antibody response in agammaglobulinemic patients. Understanding SARS-CoV-2-induced changes such as “Th-2 immunopathological variations, mononuclear cell & eosinophil infiltration of the lung and antibody-dependent enhancement (ADE)” in COVID-19 patients provides key insights to develop potential therapeutic interventions for immediate clinical management. Therefore, in this review, we have described the details of rapid detection methods of SARS-CoV-2 using molecular and serological tests and addressed different therapeutic modalities used for the treatment of COVID-19 patients. In addition, the current challenges against the development of vaccines for SARS-CoV-2 are also briefly described in this article.

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