Early detection of frailty signs is important for the senior population that prefers to keep living in their homes instead of moving to a nursing home. Sleep quality is a good predictor for frailty monitoring. Thus we are interested in tracking sleep parameters like sleep wake patterns to predict and detect poten- tial sleep disturbances of the monitored senior res- idents. We use an unsupervised inference method based on actigraphy data generated by ambient mo- tion sensors scattered around the senior’s apartment. This enables our monitoring solution to be flexible and robust to the different types of housings it can equip while still attaining accuracy of 0.94 for sleep period estimates.