# Data Gathering Techniques in WSN: A Cross-Layer View
## 6 Wearables and Wireless Body Area Networks (WBAN)
### WBAN Applications  
Even though, as stated earlier, there is no intention to provide a thorough review of WBAN applications, we mention several interesting ones. Monitoring of workers involved in extreme conditions or whose activity can be potentially dangerous (e.g., [PERSON] et al. (2014)) suggest wearable sensors for monitoring miners or construction professionals. This study evaluates integrated wearable sensors for measuring construction workers' personal level of workload, individual factors, and physiological reactions during roofing activities. A fatigue detection system for car drivers by [PERSON] et al. (2015) includes smart glasses, which are able to identify possible drowsiness by an IR detector aimed at the driver's eyes, equipped with BLE transmitter. A small processor embedded into the glasses gathers and pre-analyzes the data and then transmits them to the on-board computer in the car. The latter makes a decision about whether to issue a fatigue warning and sends it to the cloud. Wearables can be attached to a human body in order to sense the environment for the safety of the carrier, as in [PERSON] et al. (2016). The data (humidity and temperature) is then transmitted to a mobile unit, where it can be analyzed locally or further transmitted to the cloud in order to issue timely warnings. Classification of sports activity is implemented and validated by experiments by [PERSON] et al. (2017), where the activity is identified from accelerometer and ECG measurements done by chest and wrist sensors. The measurements are transmitted to a smartphone, which performs the data processing. SVM is employed for the classification. [PERSON] et al. (2018) suggest a real-time wireless body sensor networks (WBSNs) scheme for welfare assessment and disease monitoring, prevention, and treatment. The suggested scheme is composed of three components: sensing, communication, and management. Sensing attains a set of physiological parameters, such as heart rate, body temperature, ECG, temperature, blood pressure, blood glucose, heart rate, and oxygen saturation, from designated sensors embedded on a smart shirt worn by the monitored user. Communication handles the processes of delivering the sensed physiological data and controlling instructions to a backend server through wireless networks. The transmission protocols can be divided into two segments: the transfer of information from the sensors to a central terminal located at a smartphone and the transfer of information between the smartphone and the designated server, which is located on the healthcare cloud. The communication relies on multiple communication protocols including Bluetooth, WiFi, and 3G/4G (which can be replaced by 5G where available). Management is responsible for collecting, classifying, and monitoring the physiological data, and furthermore, being able to issue warning messages to medical professionals or caregivers whenever the physiological data are abnormal. Additional applications utilizing data gathering can be found in the following survey [219]. We conclude by emphasizing that the benefits of WBANs have not run their course yet. The development of data gathering will jointly progress with the ongoing advances in sensor construction and manufacturing capability, development, and standardization of Wearables 2 and beyond. Development of specialized post-processing platforms poses a specialized challenge, and the urge to make progress in this area is acute. To illustrate this, on account of an ever-growing population, specialized platforms for elderly care are needed; see, for example, the recent papers [220; 221] about wearables designated for elderly patients and references therein. Finally, wearables have been recently harnessed for combating the COVID-19 disease. Early identification of COVID-19 symptoms by evaluating the resting heart rate during the asymptomatic (presumably infectious) period and analysis by a deep learning framework is evaluated by [PERSON] and [PERSON] [222].