Internet of Things (IoT) plays a vital role in the field of healthcare. The development of smart sensors, smart devices, advanced lightweight communication protocols made the possibility of interconnecting medical things to monitor biomedical signals and diagnose the diseases of patients without human intervention and termed as Internet of Medical Things (IoMT). This paper portrays an overview of Internet of Medical Things based remote healthcare, tracking ingestible sensors, mobile health, smart hospitals, enhanced chronic disease treatment.

This paper presents a photosynthetically active radiation (PAR) sensor based on an 18-band spectral sensor chipset with integrated optical filters. PAR sensors are an important tool for farmers and the proposed solution offers a path to low-cost PAR sensors. A technique for correcting the optical spectral leakage in the spectral sensor chipset is proposed and is applied to measurements collected indoors and outdoors. Tests results show that the proposed sensor and leakage-correction technique outperform lab-grade commercially-available PAR sensors.

A photoplethysmogram (PPG) is a plethysmogram which is optically obtained, a volumetric measurement of an organ. Two PPG signals are used here to estimate the blood pressure and heart rate. Blood pressure is the pressure exerted by circulating blood upon the walls of blood vessels. Nowadays the standard blood pressure is measured by using cuff based digital devices. In cuff based method, there will be an uncomfortable to the patient and cannot measure the blood pressure in a continuous manner. Blood pressure is in terms of the systolic (maximum) pressure over diastolic (minimum) pressure and is measured in millimeters of mercury. Pulse Transit Time (PTT) is playing the main role here. PTT is the time between peaks of the two PPG signal. Heart rate can be measured by counting the number of peaks for sixty seconds. Blood pressure will be vary based on situation, activity, and disease states. Heart rate is the speed of the heartbeat measured by the number of contractions of the beat per minute (bpm). Threshold method is used to find the total number of peaks. The signals are need to be filtered to remove any noises. A low pass filter is used here to remove the noises from the PPG signals. Then the Pulse Transit Time (PTT) is determined by detecting the peak location, peak time and peak time difference using LabVIEW. Using linear regression analysis in MATLAB the blood pressure is obtained. GSM is used here to send the averaged PTT values.

This paper presents the design and development of a low-cost and low-profile sensor suitable for measuring Photosynthetic Active Radiation (PAR), which is defined as the photon flux density in 400 to 700 nm wavelength range. The sensor uses a silicon photodiode along with a bandpass optical filter and a diffuser housed inside a 3D-printed enclosure. A prototype of the sensor was built and tested with a halogen light source. Its performance was compared with research-grade commercially-available PAR sensors and with a spectroradiometer. It was found that the error of the sensor depends on light intensity being the most accurate when the light source was set to maximum intensity. The cost of the developed sensor is lower than commercial solutions, making it attractive for agricultural applications where cost is an important factor.

Physiological signal processing has significantly increased recently among the biomedical researchers for developing wearable standalone devices. Wearable devices are essential in hospital which is used for various biomedical applications such as heart rate and blood pressure measurement. In noisy environment, producing an algorithm for the physiological signal processing and feature extraction is an essential task which depends on the physiological conditions. In this paper, the software-based algorithms are developed for physiological signal processing and physiological parameter measurement. This paper presents biosensor-based real-time acquisition, processing and peak detection of ECG and PPG signals for heart rate, pulse rate and blood pressure measurement. ECG and PPG amplifiers were designed and the real-time signals are acquired through analogue devices and processed using LabVIEW and MatLab environment. The advantages of proposed work are very simple, low cost, easy integration with programming environment and gives continuous monitoring of physiological signals.