A method and related system to, among other things, automatically infer answers to all of the ADL questions and the first four questions of the IADL in the home. The inference methods detect the relevant activities unobtrusively, continuously, accurately, objectively, quantifiably and without relying on the patient's own memory (which may be fading due to aging or an existing health condition, such as Traumatic Brain Injury (TBI)) or on a caregiver's subjective report. The methods rely on the judicious placement of a number of sensors in the subject's place of residence, including motion detection sensors in every room, the decomposition of each relevant activity into the sub-tasks involved, identification of additional sensors required to detect the relevant sub-tasks and spatial-temporal conditions between the signals of sensors to formulate the rules that will detect the occurrence of the specific activities of interest. The sensory data logged on a computing device (computer, data logger etc.), date and time stamped, is analyzed using specialist data analysis software tools that check for the applicable task / activity detection rules. The methods are particularly useful for the continued in-home assessment of subjects living alone to evaluate their progress in response to medical intervention drug or physical therapy or decline in abilities that may be the indicator of the onset of disease over time. Measuring the frequency of each activity, the time required to accomplish an activity or a subtask and the number of activities / subtasks performed continuously over time can add extremely valuable quantification extensions to the existing ADL and IADL evaluation instruments, as it will not only reveal important information setting up a baseline for activity levels for each activity, but will also easily allow the detection of any drift from these personalized norms.