  New technologies are adapted to made progress in healthcare especially for
independent livings. Medication at distance is leading to integrate
technologies with medical. Machine learning methods in collaboration with
wearable sensor network technology are used to find hidden patterns in data,
detect patient movements, observe habits of patient, analyze clinical data of
patient, find intention of patients and make decision on the bases of gathered
data. This research performs comparative study on non-clinical systems in
healthcare for independent livings. In this study, these systems are
sub-divided w.r.t their working into two types: single purpose systems and
multi-purpose systems. Systems that are built for single specific purpose (e.g.
detect fall, detect emergent state of chronic disease patient) and cannot
support healthcare generically are known as single purpose systems, where
multi-purpose systems are built to serve for multiple problems (e.g. heart
attack etc.) by using single system. This study analyzes usages of machine
learning techniques in healthcare systems for independent livings. Answer Set
Programming (ASP), Artificial Neural Networks, Classification, Sampling and
Rule Based Reasoning etc. are some state of art techniques used to determine
emergent situations and observe changes in patient data. Among all methods, ASP
logic is used most widely, it is due to its feature to deal with incomplete
data. It is also observed that system using ANN shows better accuracy than
other systems. It is observed that most of the systems created are for single
purpose. In this work, 10 single purpose systems and 5 multi-purpose systems
are studied. There is need to create more generic systems that can be used for
patients with multiple diseases. Also most of the systems created are
prototypical. There is need to create systems that can serve healthcare
services in real world.
