Cardiorespiratory system monitoring using a developed acoustic sensor

This Letter proposes a wireless acoustic sensor for monitoring heartbeat and respiration rate based on phonocardiogram (PCG). The developed sensor comprises a processor, a transceiver which operates at industrial, scientific and medical band and the frequency of 2.54 GHz as well as two capacitor microphones which one for recording the heartbeat and another one for respiration rate. To evaluate the precision of the presented sensor in estimating heartbeat and respiration rate, the sensor is tested on the different volunteers and the obtained results are compared with a gold standard as a reference. The results reveal that root-mean-square error are determined <2.27 beats/min and 0.92 breaths/min for the heartbeat and respiration rate in turn. While the standard deviation of the error is obtained <1.26 and 0.63 for heartbeat and respiration rate, respectively. Also, the sensor estimate sounds of [inline-formula] to [inline-formula] obtained PCG signal with sensitivity and specificity 98.1% and 98.3% in turn that make 3% improvement than previous works. The results prove that the sensor can be appropriate candidate for recognising abnormal condition in the cardiorespiratory system

Developed wearable miniature sensor to diagnose initial perturbations of cardiorespiratory system

The progress of microelectromechanical systems tends to fabricate miniature motion sensors that can be used for various purposes of biomedical systems, particularly on-body applications. A miniature wireless sensor is developed that not only monitors heartbeat and respiration rate based on chest movements but also identifies initial problems in the cardiorespiratory system, presenting a healthy measure defined based on height and length of the normal distribution of respiration rate and heartbeat. The obtained results of various tests are compared with two commercial sensors consisting of electrocardiogram sensor as well as belt sensor of respiration rate as a reference (gold standard), showing that the root-mean-square errors obtain <2.27 beats/min for a heartbeat and 0.93 breaths/min for respiration rate. In addition, the standard deviation of the errors reaches <1.26 and 0.63 for heartbeat and respiration rates, separately. According to the outcome results, the sensor can be considered an appropriate candidate for in-home health monitoring, particularly early detection of cardiovascular system problems

Technique to estimate human reaction time based on visual perception

The design and implementation of a wearable system to estimate the human reaction time (HRT) to visual stimulus based on two identical wireless motion sensors are described. Each sensor incorporates a motion sensor (gyroscope), a processor and a transceiver operating at the industrial, scientific and medical frequency of 2.45 GHz. Relevant tests to estimate the HRT are performed in two different scenarios including simple and recognition tests for 90 pairs of measurements. The obtained results are compared with a computer-based system to determine the accuracy of the proposed system. The root mean square error, standard deviation error and mean error of the results are 2.88, 6.17 and 0.3 ms for simple test while for recognition test as low as 3.34, 7.83 and 0.35 ms, respectively. The outcomes of the HRT estimation tests confirm HRT can increase by 40–87% due to increased fatigue levels

Assessment of human gait after total knee arthroplasty by dynamic time warping algorithm

Abstract Today, the elderly population is increasing, and there are many drawbacks for them, especially defects in their knee joints which lead to improper gait. To solve this problem, their knee joint can be replaced with knee arthroplasty. In this letter, level of improvement in the human gait before and after total knee arthroplasty (TKA) surgery is investigated using the dynamic time warping (DTW) algorithm. For this purpose, several volunteers who have problems with their knees are incorporated in a test before and after TKA surgery. Then, the data of gait analysis is collected and the data is compared with a reference using the DTW algorithm. The outcome results illustrate an improvement of 89%–97% by the proposed algorithm after TKA surgery. Therefore, patients can see improvement with high accuracy and very fast that result in more use this technique in TKR surgery