The cognitive IoT sensor data preprocessing for the inhabitant’s activity state identification with unique and appropriate labeling is the primary objective of Ambient Cognition Model.
In contrast, the heterogeneous IoT sensors generate the enormous amount of data which can not be used for further machine learning analysis until the appropriate preprocessing and labeling of identified activity states have been performed. The activities identified
at the micro level represented as SA(Spot-Activity) and identification at the contextual level represented as IA(Intention-Activity) states. Therefore, the ACM model efficiently applies the threshold and weighting scheme approach for inhabitant ADL identification and extracts contextual information for further machine learning models predictive analysis.
The contribution to the knowledge from ACM has been published in IoTBDS, 2017 conference with the title of ’ A cognitive IoE approach to ambient intelligent smart home’.
The peer review assessment ensures the novelty of the proposed work and embraces the critical reviews efficiently in the further research work.
CHAPTER 4
Ambient-Expert Model : rule based time-series ADL forecasting model
Overview
This chapter describes the architecture of Ambient-Expert Model (AEM), based on the supervised machine learning approach of HMM (Hidden Markov Model) to forecast ADL(Activity Daily Living) states-patterns of the inhabitant and rule-based system to execute tasks in a proactive manner. The prime objective of AEM is to recognize the inhabitant’s activity state patterns and make predictions for pro-active task execution in the IoT enabled smart home scenario. The identified SA (Spot-Activity) and IA (Intention-Activity) from the ACM (Ambient Cognition Model) is employed as the input observation sequences into the AEM system in order to train system for the ADL pattern forecasting in time series manner. The rule-sets are applied on predicted activity states to perform pro-active task execution in the IoT enabled smart home environment. The architecture of AEM, follow a data-driven probabilistic model approach which makes system ambient intelligent to understand the surrounding situations to apply appropriate rule sets for pro-active task execution. The Ambient Expert Model (AES) is trained over time series activity states datasets and consequently tested for accuracy measures for the use case study.
4.1 AEM (Ambient Expert Model)
The AEM (Ambient Expert Model) is an important element for the design and development of modern IoT smart ambient intelligent systems. In the AEM, information about inhabitant’s activity states is vital to discover the hidden ADL (Activity Daily Living) patterns in the environment. The task of data preprocessing and feature extraction is achieved by ACM(Ambient Cognition Model). More specifically, ACM provides the initial platform for activity identification and labeling them into various SA(Spot-Activity) and IA (Intention Activity) states. Later, these activity states are used as the input datasets in AEM to perform predictive analysis on time-series data for activity pattern recognition. The application of supervised machine learning approach of HMM (Hidden Markov model) is effectively embraced in the AEM architecture. The whole ecosystem of AEM follows the principle of discrete probabilistic model, where an individual spot observation(SAi)is inferred to a specific activity state(IAi) with a higher posterior probability< (P Sh), h→ high > value as compared to low posterior probability< (P Sl) l → low > states. In AEM, the Viterbi and Baum-Welch algorithms are applied for parameter estimation in order to identify most likelihood activity Intention Activity (IA) states(IAhi) with higher probability. The identified likelihood states(IAhi), provide the recognized ADL pattern for proactive task execution. The rule-based system adapt the ADL patterns and apply certain rule-sets for proactive task execution in the environment. As Mavropoulos and Chung (2014) suggested, a decision-making system can only be successfully achieved with the expert knowledge that emulates the decision-making ability of a human expert, such that expert systems intend to emulate in all aspect of the human knowledge base to perform specific tasks. (Tran and Wagner 1999) In figure 4.1, the proposed AEM (Ambient-Expert Model) follows the discrete probabilistic approach of Hidden Markov Model(HMM) using Expectation Maximization (EM) methods to train model parameters with the Baum-Welch and Viterbi algorithms. The purpose of applying EM is to maximize the most likelihood Activity Daily living (ADL) patterns. Furthermore, the predictive regression modeling starts with historical time-series data where supervised machine learning algorithms examine the historical data and check for patterns of time decomposition, such as trends, seasonal
Fig. 4.1 The Architecture of AEM to Forecasting Time Series ADL Patterns
patterns, cyclic patterns, and regularities. Therefore, in many use case scenario organizations including product lines, financial market analysis, and product-sales patterns, uses the same approach of time series forecasting to evaluate the probable technical costs and consumers demand based on the historical datasets analysis (TechTarget n.d.).
4.1.1 Activity Cycle in Daily Routine
The individual set of activities is performed together in order to infer a specific task situation in the smart home scenarios. The figure 4.2 shown daily activity routines and related task for proactive execution through predefined rules sets for proactive and enhanced assisted living IoT environment. The behavioral changes in ADL routines could be caused by the inhabitant’s lifestyle or the other environmental factors such as
sensors placement in the smart home. For instance, weather conditions could be the most affecting constraint to influence inhabitant’s daily activity sequences and the placement of motion sensors in inappropriate space to capture inhabitant’s movement presence in a time-series manner. Therefore, in IoT sensors, it is very important to place them at appropriate place and set threshold range to capture action sequences data log in a more logical way with consideration of geographic configuration. In the table 4.1 individual activity states are labeled in a discrete manner with their appropriate aggregated weight scheme for IA(intention-activity) identification.
Fig. 4.2 Activity Cycle in Daily Routine
Table 4.1 IA(intentional-activity) state label and names based on weight aggregation value Aggregated WeightP(sai) IA (Intention Activity) Activity-Name Location
=6 1 Sleeping Bedroom
≥ 8 2 Reading on Bed Bedroom
≥ 4 3 Working Bedroom
0 0 not available Bedroom
=14 4 Working Livingroom
=16 5 Relaxing/Sitting Livingroom
≥ 18 6 Reading on sofa Livingroom
0 0 not available Livingroom
In the context of current research challenge, various pattern recognition algorithms fit into the system. More specifically, the sequential time series pattern recognition algorithms with low computing resources would be suitable to embrace the research challenge. Such as the Navies Bayesian is very effective and requires less computing resource and processing time to train the model. But in terms of results, the output of Naive Bayesian is less accurate to predict the sequential pattern with low error rate. The reason behind that is, NB only considers previous state n, (n − 1) in order to predict (n + 1) activity state.
While in current research a chain of activity states are related to each other in a sequential manner n, (n + 1...m), which is not possible to cover through the NB model. On the other hand, RNN is very effective for time series prediction with less error rate but require more computing resource and processing time to train the model. In addition, various input parameters are needed including hidden layers size, input delay, divide parameters etc.. As a result, the frequent effort required to fine-tune the model for better result outcomes. Where in the current research challenge, we only know the type of activity state as the input parameters. In addition, IoT devices have limited processing capacity to run RNN models. on the other side, HMM is a well-known method for sequential pattern recognition task. In HMM the input parameters are required once and change