# Supporting Elderly People by Ad Hoc Generated Mobile Applications Based on Vocal Interaction
## 4 Generating Apps with Vocal Interaction
### The SpeakAndSpeech Class
After a little training of about ten minutes with the system, participants performed two tasks: (_i_) execute _Intercept Call And Send SMS_, depicted in Figure 2a and (_ii_) execute _Who Is?_, in Figure 2b. The evaluation was executed at the participant's home where the hardware was previously installed. During the tasks, participants were observed by a supervisor and, after completion, they filled in a questionnaire to evaluate the user experience. We adopted the standardised user experience questionnaire of [PERSON] et al. [21], which evaluates the user experience in terms of _attractiveness_ (Overall impression of the product. Do users like or dislike it? ), _stimulation_ (Is it exciting and motivating to use the product? ), _perspiciuity_ (Is it easy to get familiar with the product? ), _dependability_ (Does the user feel in control of the interaction? ), _novelty_ (Is the product innovative and creative?) and _efficiency_ (Can users solve their tasks with the product without unnecessary effort?). Each factor's value represents the mean of four to six questions. Each question was rated on a seven-point Likert scale between opposites such as \"annoying\" (scored 1) and \"enjoyable\" (scored 7). An additional open question was proposed by the supervisor to participants for highlighting positive and negative points. Regarding the preparation of the devices involved in the controlled experiments, we used a prototype supporting the composition of MicroApps on an Android based Samsung Galaxy S4 device, Android SDK version 5.0.1 [22]. Before starting the experiment to execute the mobile app _Who is?_, we installed in each participant's home the hardware architecture depicted in Figure 12. In particular, we used an OpenHab [[http://www.openhab.org/](http://www.openhab.org/)]([http://www.openhab.org/](http://www.openhab.org/)) server installed on the Raspberry [[https://www.raspberrypi.org/](https://www.raspberrypi.org/)]([https://www.raspberrypi.org/](https://www.raspberrypi.org/)) device. The same device has also been connected to the TV, and to the doorbell and to the web camera we put on the door. ### Results

The user experience (UX) results are reported in Figure 13 as boxplots, which graphically depict groups of numerical data through their quartiles. We can note that most participants liked the system (_attractiveness_), only two scored three on average and four was neutral. All of the participants except two neutral were motivated to use the product (_stimulation_). Two people scored when rating _perspiciuity_, so for them is not easy to become familiar with the product, but the users considered themselves able to control the interaction (_dependability_). They were favorably impressed by the novelty of the system that enables them to perform their task with a reasonable effort (_efficiency_). The open questions provided the appreciation for possibility of controlling the environment by using voice. As a negative point, one participant did not like that sometimes he had to watch the

Figure 12: The hardware supporting the generated _“Who is?”_ app. screen for accomplishing his tasks. One of the participants said: _\"I liked very much to see the video on my big TV\"_. ## 6 Conclusions

In this paper, we presented an extension of the MicroApp Generator that is able to create mobile applications whose interaction is based on voice. The MicroApp Generator with a voice recognition feature is a first step towards the customization of home automation services that take into account the user needs and life styles. The vocal interface reads the screen widgets to the user and reacts to his vocal input. This kind of applications can be useful for elderly people to exploit ambient intelligent features. We also performed a preliminary evaluation on a sample of sixteen elderly people. First, results encourage us in designing other specific MicroApps, including apps for recognizing activity disorders such as falls, immobility, and empirically evaluating them.