A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Wellness Pattern Generation and Forecasting
Abstract The smart home data analysis can be divided into two parts; one, domain is activity recognition that has been discussed in the last chapter, and the other one is wellness pattern generation and forecasting. The forecasting in the WSN based smart home is the dynamic learning from the historical sensing events. Transformation of prior sensing events into pattern and forecast can be done by the analysis of knowledge discovery and soft computing techniques. There are a number of knowledge and soft computing methods available, but these methods do not perform well in the AAL environment. Either these methods are complex and needs large training data or too simple where they offer poor accuracy (Moutacalli et al. 2015; Pulsford et al. 2011; Candás et al. 2014). For the Wellness Protocol based AAL the time series approach has been proposed and implemented. This time series approach includes the seasonal parameters from last year; it does not demand too much learning data. The rest of the chapter includes the wellness forecasting analysis and comparative results with other existing data mining methods.
Wellness Pattern Generation and Forecasting
Abstract The smart home data analysis can be divided into two parts; one, domain is activity recognition that has been discussed in the last chapter, and the other one is wellness pattern generation and forecasting. The forecasting in the WSN based smart home is the dynamic learning from the historical sensing events. Transformation of prior sensing events into pattern and forecast can be done by the analysis of knowledge discovery and soft computing techniques. There are a number of knowledge and soft computing methods available, but these methods do not perform well in the AAL environment. Either these methods are complex and needs large training data or too simple where they offer poor accuracy (Moutacalli et al. 2015; Pulsford et al. 2011; Candás et al. 2014). For the Wellness Protocol based AAL the time series approach has been proposed and implemented. This time series approach includes the seasonal parameters from last year; it does not demand too much learning data. The rest of the chapter includes the wellness forecasting analysis and comparative results with other existing data mining methods.
Wellness Pattern Generation and Forecasting
Ghayvat, Hemant (author) / Mukhopadhyay, Subhas Chandra (author)
2017-01-01
13 pages
Article/Chapter (Book)
Electronic Resource
English
Activity Detection and Wellness Pattern Generation
| Springer Verlag | 2017
| Online Contents | 2013
Online Contents | 2012
Online Contents | 2011
| Online Contents | 2008