# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 6 Systems for HAR
### Related Work
Next, they tested the recognition system on the 11 sequences of accurate data, and 77% of these patterns were detected. [PERSON] et al. [79] proposed an interesting application based on sensory data acquired through a smartphone and a convolutional long short-term memory (ConvLSTM) for the classification of human activities within an indoor environment network. They considered nine activities for classification. The authors associated the recognition system with a pre-existing positioning system with which they evaluated human activities considering different test paths in a multistory building obtaining an average positioning error of 2.4 m. Through inertial sensors, the internal positioning system can recognize the type of activity, e.g., a flat path or a climb of stairs. They also integrated the proposed HAR model with the fingerprint-based positioning system used by [PERSON] et al. [80]. A filter fusion system was also used to combine several sources of information. Moreover, more than 84% of floor transitions were correctly identified and performed in those routes, better estimating the user's current position in a multistory building. The results showed that combining the recognition of activities with the positioning system improved the identification results. [PERSON] [81], to create an innovative system in which individuals are not required to wear any device, proposed an approach for location and asset recognition using passive RFID tags. The project aims to help older people live longer independently and safely in their own homes, with minimal support from their caregivers. The author structured the application with three modules: the wireless RFID sensor network (WRSN), activity discovery (AD), and activity monitoring (AM). The WRSN module verifies the applicability of RFIDs and wireless sensors for identifying the location and activities of individuals. The sensory data and RFIDs collected with the previous module are extracted and grouped with the AD module to detect individuals' routine activities automatically. Lastly, the AM module recognizes the actions and leases they are carried out. To achieve the intended goal, a series of passive tags and a reader equipped with antennas create an RSS field that ensures that the RSSI signal covers the area under consideration. RSSI vectors from known locations are used to train the classifier, which later predicts a position reported by a new RSSI vector. For the tracking phase, the author used a traditional kNN model enriched with probabilistic information to evaluate the probability of the positions obtainable from the RSSIs vectors used for constructing the emission matrix in the hidden Markov model (HMM). In the phase after obtaining the transmission matrix, the author applied the Viterbi search algorithm to determine the most likely path of the subject. For the reconnaissance phase of human activities, an array of passive RFID tags is placed within the area under consideration. Lastly, the basic principle of sparse representation is applied to create the activity recognition model. With the help of dictionary-based learning, structural information is identified among the RSSI signals of the different activities. Meanwhile, a feature selection method was used to extract signal patterns based on filters for canonic correlation analysis. The last phase of the study focused on analyzing the activities related to the residential context, verifying whether the elderly usually carry out their daily activities or are found abnormal, such as requiring timely assistance. For example, an activity reasoning engine based on a suitable sensor based on Open Cyc, the world's largest and most complete common-sense knowledge base with more than 300,000 concepts, has been developed for this phase. The study did not report the accuracy values found in the estimates made. Furthermore, [PERSON] et al. [82] adopted an integrated system based on UHF RFID passive tags and KNN method. A landmark reference tag grid was applied in their system.