Wearable technologies such as smart watches have become increasingly popular in recent years. Not only do they help keep us on time and connected with the digital world, they also have numerous sensors that can tell us about our health. There are inertial sensors that count the number of steps we take and photoelectric sensors that can measure our heart rate. Driving much of the processing of this data is now machine learning. My research involves using machine learning to understand trends in people's health and wellness from their smartwatch data.
What is machine learning?
Machine learning models are algorithms that learn from data to then make predictions on new data. Nowadays, machine learning is prevalent throughout our daily lives, from ChatGPT to song and video recommendations.
What are the advantages of machine learning?
Machine learning in wearables takes data collected by the wearable and transforms it into meaningful health and wellness insights. For instance, smart watches can count the number of exercise repetitions performed or the quality of a night's sleep using sensor data. These data points can then provide insight on our general wellbeing.
What are the disadvantages of machine learning?
