User Activities Outliers Detection; Integration of Statistical and Computational Intelligence Techniques

Abstract

In this paper, a hybrid technique for user activities outliers detection is introduced. The hybrid technique consists of a two-stage integration of Principal Component Analysis (PCA) and Fuzzy Rule-Based Systems (FRBS). In the first stage, the Hamming distance is used to measure the differences between different activities. PCA is then applied to the distance measures to find two indices of Hotelling's T 2 and Squared Prediction Error. In the second stage of the process, the calculated indices are provided as inputs to the FRBSs to model them heuristically. The model is used to identify the outliers and classify them. The proposed system is tested in real home environments, equipped with appropriate sensory devices, to identify outliers in the activities of daily living of the user. Three case studies are reported to demonstrate the effectiveness of the proposed system. The proposed system successfully identifies the outliers in activities distinguishing between the normal and abnormal behavioural patterns.

Hidden State Conditional Random Field for Abnormal Activity Recognition in Smart Homes

“…The MLV and MLV index of normal activities 1-5 are (1, 326), (2, 100), (3,573), (4,195) and (5,364), where the first value in brackets are the MLV indexes, and the second value in brackets are the MLVs. For each testing activity, we compute the likelihood vector of the observation sequence based on the trained HCRF, and find out the MLV and MLV index.…”

“…The MLV and MLV index of activities 1-5 are denoted as (1, 326), (2, 100), (3,573), (4,195) and (5,364). Then, we compute the likelihood vectors of the observation sequences of testing activities 1-7 based on the trained HCRF, respectively.…”

“…The MLV and MLV index of training activities 1-6 are denoted as (1, 269), (2,82), (3,475), (4, 160), (5, 299) and (6,61). For testing activities 1-7, we compute the likelihood vectors of the observation sequences based on the trained HCRF, respectively, and the likelihood vectors of testing activities 1-7 are shown in Table 7 and the visualization of likelihood vectors is shown in Figure 9.…”

“…As age-related changes in the brain cause a decline in short-term memory and slow learning ability, elderly people run into all sorts of barriers in performing their daily routine tasks [3]. Thus, to better care for elderly people, we not only need to know what they are doing, but also need to know if there are some abnormal activities [4].…”

An Ambient Assisted Living Technology Platform for Informal Carers of the Elderly - iCarer

“…Registered data by the monitoring system will be analysed and fed into the iCarer platform in order to model the ADC based on behavioural patterns. Based on this information, the platform can infer possible problems in the informal carer's activities/status [8]. Thus, the platform will act as a "virtual care" providing support to the carer with recommendations to solve the identified issue.…”