## 3 Related Work
The authors of [49] suggested a low-energy oximeter for pulse detection with the aim of enhancing wearability. Two approaches can be used to minimize power absorption, the first of which is lower SNR (signal-to-noise-ratio) monitoring, which regularly calculates the SNR and regulates the stretch of duration, causing an LED to be in the \"ON\" condition. With higher SNR values, the LED reflects accurate readings. The second approach is phase-locked loop tracking, which calculates the probability of troughs and peaks of a photoplethysmographic signal, providing crucial real-time information. The authors of [50] designed a pulse oximeter based on ear reflection, which was designed to identify the level of blood oxygen even when a patient is suffering from hypothermia, shock, fear etc., which may cause blood systematization, increasing the chances of an unidentified pulse rate in the fingertips. Reasonable precision was achieved in surgical patients; however, the authors concluded that these sensors can be used in combination with wearable pulse oximeters on the fingers but not as an alternative. The authors of [51] suggested an oximeter that can be worn on the wrist that is concave in structure, avoiding external light, with improved noise resistance. The proposed device can also measure skin temperature in combination with three crucial sensors in a single wearable node. Many other wearable sensors have been proposed with a focus on tracking the specific condition of patients. ECGs (echocardiograms) are among the most common methods to measure heart-related issues, and various wearable devices have been developed to gather these signals. The authors of [52] suggested a helmet with an electroencephalogram (EEG)-enabled sensor feature to measure the activity of the brain to identify conditions such as sleep disorders, seizures and progress after head injury. The authors of [53; 54] suggested an EEG system for a specific purpose such as driver drowsiness identification or stress management in the form of a wearable headband EEG system to measure activity. The authors of [52] suggested an ECG sensor based on an armband to provide measurements with reasonable precision. The authors of [55] discussed a triaxial accelerometer embedded inside a cellphone (smartphone) using a machine learning approach to detect the posture of the user using a suitable algorithm, achieving an accuracy of 90%. The classification approach for the detection of posture was found to be less accurate for fall detection [56], with an accuracy of 89% in an outdoor environment and 93% in an indoor environment. In [57], a previously proposed camera-based wearable system was combined with an accelerometer, achieving 91% precision in fall identification. In [58; 59] a gyroscope, accelerometer and magnetometer were used to accurately identify falls also; the authors also added a barometer for accurate identification of falls based on changes in height. In further work, accuracies of 98 to 100% were achieved in fall detection, and the authors suggested the immediate implementation of the proposed system in healthcare settings. For elderly patients, the gait identification method is quite useful to obtain knowledge of specific conditions such as Parkinson's disease. Gait identification was considered in patients with [PERSON]'s disease and those healing from a stroke in [60] using a wearable foot sensor designed to measure various parameters, such as walking speed, step length, etc. In [61], a sensor developed to control prosthetics of the lower limb was used for gait detection in lower-limb amputees. The authors of [62] suggested placing three accelerometers on the ankle, knee and hip of [PERSON]'s patients. A feature extraction approach was used in combination with an anomaly detection approach for gait freeze and anomaly identification in [63] using waist-worn embedded devices with microcontrollers and accelerometers (triaxial) to track gait.