Application of Embedded Smart Wearable Device Monitoring in Joint Cartilage Injury and Rehabilitation Training

Joint injuries cause varying degrees of damage to joint cartilage. The purpose of this paper is to study the application of embedded smart wearable device monitoring in articular cartilage injury and rehabilitation training. This paper studies what an embedded system is and what a smart wearable device is and also introduces the rehabilitation training method of articular cartilage injury. We cited an embedded matching cost algorithm and an improved AD-Census. The joint cartilage damage and rehabilitation training are monitored. Finally, we introduced the types of smart wearable devices and different types of application fields. The results of this paper show that, after an articular cartilage injury, the joint function significantly recovers using the staged exercise rehabilitation training based on embedded smart wearable device monitoring. We concluded that, from 2013 to 2020, smart wearable devices are very promising in the medical field. In 2020, the value will reach 20 million US dollars.

A Stretchable Strain Sensor Based on CNTs/GR for Human Motion Monitoring

Flexible/stretchable strain sensors have attracted much attention due to their advantages for human-computer interaction, smart wearable and human monitoring. However, there are still great challenges on gaining super durability, quick response, and wide sensing range. This paper provides a simple process to obtain a sensor which is based on graphene (GR)/carbon nanotubes (CNTs) and Ecoflex hybrid, which demonstrates superb endurance (over 1000 cycles at 100% strain), remarkable sensitivity (strain over 125% sensitivity up to 20) and wide sensing range (175%). All results indicate that it is capable for human movement monitoring, such as finger and knee bending and pulse beat. Most importantly, it can be used as a warning function for the night cyclist’s ride. This research provides the feasibility of using this sensor for practical applications.

High Effective Sensitivity, Wide Working Range of Melt-blown Nonwoven Conductive Substrates for Smart Wearable Strain Sensors

High sensitivity, wide working range and flexible portability of strain sensors are crucial for smart wearable applications. To obtain these performances, several elastic melt-blown nonwoven substrates with excellent flexibility and high conductivity were developed by loading with polypyrrole through a double-dipping and double-rolling finishing method. The structure and conductivity are characterized by scanning electron microscope, infrared spectrometer, digital source meter and so on. The results indicate that the conductivity of prepared substrates is affected by the pyrrole concentration and polypyrrole amount deposited in nonwovens. Obviously, the conductivity and sensitivity of substrates as strain sensors are highly and positively correlated to the fiber orientation in nonwovens, and the effective working range and corresponding sensitivity of sensors are determined by the elastic deformation interval of melt-blown substrate. When the pyrrole concentration is 5.5%, the nonwoven substrate with 45.30% porosity possesses the anisotropic optimal conductivity with 23.491 S m-1 along winding direction and 15.063 S m-1 along width direction. Moreover, the as-prepared flexible conductive substrate exhibits the characteristics of wide working strain range (0-24.2%), high sensitivity with 1.94 gauge factor at the range, fast response (0.023 s), tiny hysteresis (0.011s), high durability and stability after 1000 cycles. Furthermore, the as-prepared sensor provides an effective method to prepare smart wearable strain sensors used as the monitor of finger bending in details and the precise recognition of human voice changes.

Leveraging Smartphone Sensors for Detecting Abnormal Gait for Smart Wearable Mobile Technologies

Walking is one of the most common modes of terrestrial locomotion for humans. Walking is essential for humans to perform most kinds of daily activities. When a person walks, there is a pattern in it, and it is known as gait. Gait analysis is used in sports and healthcare. We can analyze this gait in different ways, like using video captured by the surveillance cameras or depth image cameras in the lab environment. It also can be recognized by wearable sensors. e.g., accelerometer, force sensors, gyroscope, flexible goniometer, magneto resistive sensors, electromagnetic tracking system, force sensors, and electromyography (EMG). Analysis through these sensors required a lab condition, or users must wear these sensors. For detecting abnormality in gait action of a human, we need to incorporate the sensors separately. We can know about one's health condition by abnormal human gait after detecting it. Understanding a regular gait vs. abnormal gait may give insights to the health condition of the subject using the smart wearable technologies. Therefore, in this paper, we proposed a way to analyze abnormal human gait through smartphone sensors. Though smart devices like smartphones and smartwatches are used by most of the person nowadays. So, we can track down their gait using sensors of these intelligent wearable devices. In this study, we used twenty-three (N=23) people to record their walking activities. Among them fourteen people have normal gait actions, and nine people were facing difficulties with their walking due to their illness. To do the stratification of the gait of the subjects, we have adopted five machine learning algorithms with addition a deep learning algorithm. The advantages of the traditional classification are analyzed and compared among themselves. After rigorous performance analysis we found support vector machine (SVM) showing 96% accuracy, highest among the tradition classifiers. 70%, 84%, and 95% accuracy is obtained by the logistic regression, Naïve Bayes, and k-Nearest Neighbor (kNN) classifiers, respectively. As per the state-of-the art, deep learning classifiers has been proven to outperform the traditional classifiers in similar binary classification problems. We have considered the scenario and applied the 2D convolutional neural network (2D-CNN) classification algorithm, which outperformed the other algorithms showing accuracy of 98%. The model can be optimized and can be integrated with the other sensors to be utilized in the mobile wearable devices.

Patient and Provider Perspective of Smart Wearable Technology in Diabetic Foot Ulcer Prevention: A Systematic Review

Background and Objectives: Smart wearable devices are effective in diabetic foot ulcer (DFU) prevention. However, factors determining their acceptance are poorly understood. This systematic review aims to examine the literature on patient and provider perspectives of smart wearable devices in DFU prevention. Materials and Methods: PubMed, Scopus, and Web of Science were systematically searched up to October 2021. The selected articles were assessed for methodological quality using the quality assessment tool for studies with diverse designs. Results: A total of five articles were identified and described. The methodological quality of the studies ranged from low to moderate. Two studies employed a quantitative study design and focused on the patient perspective, whereas three studies included a mixed, quantitative/qualitative design and explored patient or provider (podiatrist) perspectives. Four studies focused on an insole system and one included a smart sock device. The quantitative studies demonstrated that devices were comfortable, well designed and useful in preventing DFU. One mixed design study reported that patients did not intend to adopt an insole device in its current design because of malfunctions, a lack of comfort. and alert intrusiveness, despite the general perception that the device was a useful tool for foot risk monitoring. Two mixed design studies found that performance expectancy was a predictor of a podiatrist’s behavioural intention to recommend an insole device in clinical practice. Disappointing participant experiences negatively impacted the podiatrists’ intention to adopt a smart device. The need for additional refinements of the device was indicated by patients and providers before its use in this population. Conclusions: The current evidence about patient and provider perspectives on smart wearable technology is limited by scarce methodological quality and conflicting results. It is, thus, not possible to draw definitive conclusions regarding acceptability of these devices for the prevention of DFU in people with diabetes.

Deep Learning Based Indian Sign Language Words Identification System

In Indian Population there is about 1 percent of the people are deaf and dumb. Deaf and dumb people use gestures to interact with each other. Ordinary humans fail to grasp the significance of gestures, which makes interaction between deaf and mute people hard. In attempt for ordinary citizens to understand the signs, an automated sign language identification system is proposed. A smart wearable hand device is designed by attaching different sensors to the gloves to perform the gestures. Each gesture has unique sensor values and those values are collected as an excel data. The characteristics of movements are extracted and categorized with the aid of a convolutional neural network (CNN). The data from the test set is identified by the CNN according to the classification. The objective of this system is to bridge the interaction gap between people who are deaf or hard of hearing and the rest of society.

Enhanced IoT Based Child Missing Alert System Using RSSI

Our proposed system discusses the concept of a smart wearable device connected to their parent’s mobile phone for children and their parents respectively. In this project we propose that to let the system be divided into three parts, namely the safe, intermediate and danger zones. If the child is within the safe zone, then no buzzer is sounded whereas if the child is in the intermediate range a buzzer alert will be sounded. If the child crosses the ‘danger’ zone, the buzzer is sounded with an immediate notification sent to the parent. In case the child goes out of danger zone, a GPS module is attached that would help parent know the exact location of the child once he/she is outside the 100meters of radius from the parent. This project also has features to sense the child’s temperature and heartbeat along with notifying the child’s parent in case the child has an accident using the temperature, heartbeat and pressure sensors respectively. The RSSI is used for distance sensing whereas GSM is used for notification sending to the parent’s mobile phone.