This project aims to design and implement a fall detection system for the elders using machine learning techniques and Internet-of-Things (IoT). The main issue with fall detection systems is false alarms and hence incorporating machine learning in the fall detection algorithm can tackle this problem. Therefore, choosing the right machine learning algorithm for the given problem is essential and several factors need to be considered in making that choice. For this project, the XGBoost algorithm is used and the machine learning model is trained on the Sisfall dataset. A wearable device that is worn on the waist is designed using an accelerometer, a microcontroller, a Global Positioning System (GPS) module and a buzzer. The acceleration data obtained is converted into features and fed into the machine learning model which will then make a prediction. If a fall event has occurred, the buzzer is activated and emergency contacts of the victim are notified immediately using IoT and Global System for Mobile Communications (GSM). This allows the fall victim to be attended quickly, thus reducing the negative consequences of the fall. The details of the fall are stored on the cloud so that they can be easily accessed by healthcare professionals.Testing the system concluded that the XGBoost machine learning algorithm is well suited for this problem due to the small percentage error obtained.

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An adaptive forward error control and modulation scheme is proposed for slow Rayleigh fading channels. The adaptive scheme employs a family of RS codes of different rates and Multiple Phase Shift Keying (MPSK) modulation schemes according to the states of the channel. This uses a channel estimator to select the most appropriate codulation scheme. Simulation results show that the adaptive scheme gives significant improvement in the BER and throughput performance.