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Fig. 2. Internet users per 100 inhabitants 1997 to 2007; ICT Data and Statistics (IDS), International Telecommunication Union (ITU). Retrieved 25 May 2015. Archived 17 May 2015 at the Wayback Ma- chine.

Figure 2 Internet users per 100 inhabitants 1997 to 2007; ICT Data and Statistics (IDS), International Telecommunication Union (ITU). Retrieved 25 May 2015. Archived 17 May 2015 at the Wayback Ma- chine.

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Fig. 1. Application areas of ambient intelligence (own source).
Fig. 1. Application areas of ambient intelligence (own source).
Fig. 3. Error of DNNs on ImageNet competition over time. Source: devblogs.nvidia.com.
Fig. 3. Error of DNNs on ImageNet competition over time. Source: devblogs.nvidia.com.
Fig. 4. The key factors for ambient intelligence in assisted living (from [83]). As shown in Fig. 4, when creating a human-centric smart environment for assisted living, two of the most fundamental tasks are to (a) classify normal daily ac- tivities; and (b) detect abnormal activities. Examples of ambient assisted living for the elderly include detect- ing anomalies like falling, robbery or fire at home, rec- ognizing daily living patterns, and obtaining the statis- tics associated with various daily activities over time. Among all the activities, detecting a fall is one of the topics attracting the most attention, due to the associ- ated risks such as bone fracture, stroke or even death. Triggering emergency help is often necessary, espe- cially for people who live alone. Many different meth- ods have been developed by exploiting different types of sensors, e.g., smart watches, sound sensors, wear- able motion devices (gyroscopes, speedometers, ac- celerometers), visual, range and infrared (IR) cameras. Each sensor has its pros and cons, for example, a wear- able device is straightforward; however, it requires reg-
Fig. 4. The key factors for ambient intelligence in assisted living (from [83]). As shown in Fig. 4, when creating a human-centric smart environment for assisted living, two of the most fundamental tasks are to (a) classify normal daily ac- tivities; and (b) detect abnormal activities. Examples of ambient assisted living for the elderly include detect- ing anomalies like falling, robbery or fire at home, rec- ognizing daily living patterns, and obtaining the statis- tics associated with various daily activities over time. Among all the activities, detecting a fall is one of the topics attracting the most attention, due to the associ- ated risks such as bone fracture, stroke or even death. Triggering emergency help is often necessary, espe- cially for people who live alone. Many different meth- ods have been developed by exploiting different types of sensors, e.g., smart watches, sound sensors, wear- able motion devices (gyroscopes, speedometers, ac- celerometers), visual, range and infrared (IR) cameras. Each sensor has its pros and cons, for example, a wear- able device is straightforward; however, it requires reg-
Fig. 5. GUI for a sample assisted-living system that recognizes typ- ical activities of the elderly at home (from [82]).
Fig. 5. GUI for a sample assisted-living system that recognizes typ- ical activities of the elderly at home (from [82]).
Fig. 6. A 2D convolutional network in combination with the mul- timodal information fusion of MRI for the grading of brain-tumour gliomas (from [31]).
Fig. 6. A 2D convolutional network in combination with the mul- timodal information fusion of MRI for the grading of brain-tumour gliomas (from [31]).
Fig. 7. Screenshots of an AmI-assisted e-learning system to assess the learner’s responses as attentive (left) and distracted (right) on a tablet PC (from [67]). Besides facial analytics (FA), there are many AmlI- assisted or intelligent tutoring systems [66] that em- ploy AmI methods, including classification algorithms
Fig. 7. Screenshots of an AmI-assisted e-learning system to assess the learner’s responses as attentive (left) and distracted (right) on a tablet PC (from [67]). Besides facial analytics (FA), there are many AmlI- assisted or intelligent tutoring systems [66] that em- ploy AmI methods, including classification algorithms
Fig. 8. Plots of the actual versus predicted hourly energy consump- tion for a specific floor of a smart-building application using a deep-learning network (from [1]).
Fig. 8. Plots of the actual versus predicted hourly energy consump- tion for a specific floor of a smart-building application using a deep-learning network (from [1]).
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