Sleep is a natural requirement of human beings that is common across most mammal species. The duration of sleep periods each day is a key factor to health status and, in general, to a person's health and wellbeing. In face of those facts, it is important to have a measurement of sleep duration and bodily activity during this sleep. In the last years some fitness devices in the market have been designed to track activity and provide indications about calorie expenditure as well as intake, along with specific sleep indicators. Such measures lack precision, as most of those are wrist worn devices and thus providing intelligence about upper arm movement while lacking in information about the steadiness of the human body as a whole. The present work explores some existing static solutions in the market and provides a feasible alternative for sleep movement detection. The research aims to study the sleep patterns of cancer patients that have undergone treatment cycles, thus providing indicators about their mental health status.