A Low-Cost Pressure Sensor Matrix for Activity Monitoring in Stroke Patients Using Artificial Intelligence

Fatema, Anis; Poondla, Surya; Mishra, Rishabh; Hussain, Aftab M.

doi:10.1109/jsen.2021.3054406

Cited by 29 publications

(10 citation statements)

References 37 publications

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“…The collected exercise data of the trainer is combined with the real-time data of data processing, motion capture, and human body model drive module, and the standard sports action is compared with the exercise data of the trainer, and the wrong action information is displayed on the screen (5) System Scoring Module. The comparison between the user's motion data and the standard motion data, and the user's real-time score when learning the action is calculated according to the harshness of the action (i.e., the threshold angle) set before the user enters the system (6) Compare the angle data of the trainer's human skeleton model with the angle of the standard gymnastics training items, determine the threshold range, and evaluate the degree of movement standard in the form of scores [20] The relationship between the modules is shown in Figure 3.…”

Section: Design Of Gymnastics Action Recognition Methodmentioning

confidence: 99%

Gymnastics Action Recognition and Training Posture Analysis Based on Artificial Intelligence Sensor

Chen

2022

Journal of Sensors

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In order to solve the problems that the gymnastics action recognition system cannot select gymnastics training items and difficulty modes, the calculation matching degree and its threshold angle are inaccurate, the effect and efficiency of action learning are low, and this research proposes a gymnastics action recognition and training pose analysis methods based on artificial intelligence sensors. This method completes the performance improvement of the traditional human action recognition algorithm and uses the skeletal features of the Kinect sensor to discriminate sports actions. Clustering based on static K -means algorithm increases the accuracy of pose selection, and each pose is recognized by human action using artificial neural network (ANN) and hidden Markov model (HMM). The obtained results are as follows: comparison of nonstatic and proposed static K -means algorithm on the training set and the overall accuracy of the proposed method is much better than the previous method. Among the four movements, the accuracy rate of “sitting” and “standing” movements is significantly higher, reaching 100%. In the gymnastics action recognition experiment, the average recognition rate of the system in this research is 93.6%, the false rejection rate is 5%, and the false acceptance rate is only 1.4%. It is proved that the system interface designed in this research can prompt the part that needs to be corrected, display the error on the output device, more efficiently assist the user to perform targeted training on the action to be learned, and improve the effect and efficiency of action learning.

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Section: Design Of Gymnastics Action Recognition Methodmentioning

confidence: 99%

Gymnastics Action Recognition and Training Posture Analysis Based on Artificial Intelligence Sensor

Chen

2022

Journal of Sensors

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“…However, in practical applications, these can also be affected by certain environmental factors. In the figure, different sensors can be deployed at different body locations to monitor physical conditions by applying sensors in wearable and implantable devices as carriers, which can be attached to the subject's body to gauge parameters such as EEG [13], glucose [14][15][16][17][18][19][20], heartbeat [21], exercise [22,23], pH [24][25][26], pressure [27][28][29][30], skin perception [31], breathing [32], and epilepsy [33]. These data can then be transmitted via the device (RFID or WiFi) to the IoT cloud infrastructure and made available to the target user.…”

Section: Rfid Sensor-based Iot Healthmentioning

confidence: 99%

“…Pressure [27,29] and strain [28,30] sensors are among the most standardised applications of flexible sensors. To date, the monitoring of various physiological parameters, such as medical bandages and gloves, is successfully demonstrated with various piezoresistive and piezoelectric sensors [66,67].…”

Section: Health Monitoring Sensors and Application Scenariosmentioning

confidence: 99%

“…Some pressure sensors [68] form the electrodes by coalescing two nanoparticles as electronic bandages, allow-ing operation in different media. Other research on these pressure sensors is in tactile perception [31] and artificial intelligence [30]. Smart materials attract intensive interest due to their great potential.…”

Section: Health Monitoring Sensors and Application Scenariosmentioning

confidence: 99%

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Prospective RFID Sensors for the IoT Healthcare System

Xiang

Zhao²,

Tian

et al. 2022

Journal of Sensors

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The outbreak of COVID-19 has attracted people’s attention to our healthcare system, stimulating the advancement of next-generation health monitoring technologies. IoT attracts extensive attention in this advancement for its advantage in ubiquitous communication and sensing. RFID plays a key role in IoT to tackle the challenges in passive communication and identification and is now emerging as a sensing technology which has the ability to reduce the cost and complexity of data collection. It is advantageous to introduce RFID sensor technologies in health-related sensing and monitoring, as there are many sensors used in health monitoring systems with the potential to be integrated with RFID for smart sensing and monitoring. But due to the unique characteristics of the human body, there are challenges in developing effective RFID sensors for human health monitoring in terms of communication and sensing. For example, in a typical IoT health monitoring application, the main challenges are as follows: (1) energy issues, the efficiency of RF front-end energy harvesting and power conversion is measured; (2) communication issues, the basic technology of RFID sensors shows great heterogeneity in terms of antennas, integrated circuit functions, sensing elements, and data protocols; and (3) performance stability and sensitivity issues, the RFID sensors are mainly attached to the object to be measured to carry out identification and parameter sensing. However, in practical applications, these can also be affected by certain environmental factors. This paper presents the recent advancement in RFID sensor technologies and the challenges for the IoT healthcare system. The current sensors used in health monitoring are also reviewed with regard to integrating possibility with RFID and IoT. The future research direction is pointed out for the emergence of the next-generation healthcare and monitoring system.

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“…Typically, the paper sensors are mainly composed of a flexible paper substrate and conductive materials. Different electrode and sensitive materials, for example, gold, silver, copper, carbon nanotubes, MoS 2 , and MoSe 2 , dramatically increase the cost and electrical wastes and result in serious environmental pollution, which therefore weaken the advantages of paper sensors. − Motivated by the urgent demand for the emerging green electronics, developing more cost-efficient biodegradable paper sensors is essential. Another key factor that confines the development of the paper sensors lies on the discrimination of electrical signals toward different stimuli.…”

Section: Introductionmentioning

confidence: 99%

Calligraphy and Kirigami/Origami-Inspired All-Paper Touch–Temperature Sensor with Stimulus Discriminability

Liu

Sun

Tong

et al. 2022

ACS Appl. Mater. Interfaces

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The use of cost-effective renewable raw materials to develop electronic devices has been strongly demanded for sustainable and biodegradable green electronics. Here, by taking inspiration from the traditional calligraphy and kirigami/origami arts, we show a novel cuttable and foldable all-paper touch–temperature sensors fabricated by simply brushing the carbon black ink onto the cellulose paper followed by a layer–layer lamination strategy. The use of environmentally friendly common commodities in daily life including carbon black ink and cellulose paper as the main component materials of sensors effectively lowers the cost and has positive impacts on the environment and health. The sensors can be freely cut or folded into the targeted shapes and can even reversibly morph between 2D and 3D configurations without affecting device function. Additionally, the sensors show a discrimination capability toward pressure and temperature. Our fabrication strategy provides a promising approach for creating the low-cost eco-friendly sensors with a versatile pattern design and a morphing shape without sacrificing the global structural integrity and device functionality.

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A Low-Cost Pressure Sensor Matrix for Activity Monitoring in Stroke Patients Using Artificial Intelligence

Cited by 29 publications

References 37 publications

Gymnastics Action Recognition and Training Posture Analysis Based on Artificial Intelligence Sensor

Gymnastics Action Recognition and Training Posture Analysis Based on Artificial Intelligence Sensor

Prospective RFID Sensors for the IoT Healthcare System

Calligraphy and Kirigami/Origami-Inspired All-Paper Touch–Temperature Sensor with Stimulus Discriminability

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