Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system.

Respiratory rate is an important parameter for many health, home care, work, or sport applications. In this paper, a new wearable sensing system based on a piezoresistive FlexiForce sensor has been developed. The sensor can be attached to any common chest strap. A compact 3D casing has been designed and printed with a 3D printer. This casing integrates the sensor and all auxiliary elements of the system: microcontroller, battery, Bluetooth module, connections, battery charger, and acquisition circuit. To the best of our knowledge, this is the first study presenting a FlexiForce respiration sensor that includes all system elements in a single compact casing. The source files with the design of the casing have been published as supplementary material to be reused by any interested researcher. The sensing system was tested with twenty-one subjects for different breathing rates. Two different algorithms were developed to obtain the respiratory rate from the voltage signals recorded by the sensor. Statistical tests were performed to determine the optimal computation time window and algorithm. This approach is also novel in this field. Low error values were obtained for a time window of 27 s with an algorithm based on the calculation of time between zero-crossings (4.02%) and with an algorithm based on counting them (3.40%). To promote research transparency and reusability, the dataset with the recorded data and the source code of the algorithms and statistical tests have also been published. Therefore, an open, replicable, low-error, wearable, wireless, and compact sensing system to measure respiratory rate was developed and tested.

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Sensors for respiratory monitoring can be classified into wearable and non-wearable systems. Wearable sensors can be worn in several positions, the chest being one of the most effective. In this paper, we have studied the performance of a new piezoresistive breathing sensing system to be worn on the chest with a belt. One of the main problems of belt-attached sensing systems is that they present trends in measurements due to subject movements or differences in subject constitution. These trends affect sensor performance. To mitigate them, it is possible to post-process the data to remove trends in measurements, but relevant data from the respiration signal may be lost. In this study, two different detrending methods are applied to respiration signals. After conducting an experimental study with 21 subjects who breathed in different positions with a chest piezoresistive sensor attached to a belt, detrending method 2 proved to be better at improving the quality of respiration signals.