Many people in the world are affected by muscle wasting, especially the population hits by myotonic dystrophy type 1 (DM1). Those people are usually given a program of multiple physical exercises to do. While DM1 and many other people have difficulties attending commercial centers to realize their program, a solution is to develop such a program completable at home. To this end, we developed a portable system that patients could bring home. This prototype is an improved version of the previous one using Wi-Fi, as this new prototype runs on BLE technology. This new prototype conceptualized induces great results.

The Internet of things has profoundly changed the way we imagine information science and architecture, and smart homes are an important part of this domain. Created a decade ago, the few existing prototypes use technologies of the day, forcing designers to create centralized and costly architectures that raise some issues concerning reliability, scalability, and ease of access which cannot be tolerated in the context of assistance. In this paper, we briefly introduce a new kind of architecture where the focus is placed on distribution. More specifically, we respond to the first issue we encountered by proposing a lightweight and portable messaging protocol. After running several tests, we observed a maximized bandwidth, whereby no packets were lost and good encryption was obtained. These results tend to prove that our innovation may be employed in a real context of distribution with small entities.

The strong integration of “intelligent mobile devices” into modern societies offers a great potential for a wide spread distribution of mobile serious games. As in the case of Virtual Reality based systems, in order to be useful and efficient, these serious games need to be validated ecologically. In this context, this paper addresses the use of ecological interactions for a mobile serious game. We exploit a wearable insole in order to let users interact with a virtual soccer game via real-world soccer movements. We analyzed the concept of ecological interactions. The system used for recognition of ecological gestures is also detailed. A primary study showed that proposed system can be exploited for real time gesture recognition on a mobile device.

This paper describes a novel method for recognizing different soil types based on inertial data generated by a user's gait. To achieve this objective, a new wearable device which aims at collecting data produced by an embedded 6-axis accelerometer/gyroscope was designed first. To command this piece of hardware (start and stop recording, as well as annotate raw data), a mobile application was specifically developed. A total of 70 well-known features both from time and frequency domains that are mostly used in activity recognition were computed over each signal to produce enough discriminating characteristics. Then, two machine learning algorithms (Random Forest and k-Nearest Neighbors) were employed to classify such data. The proposed method was tested with 9 participants on four soil types with an experimental setup close to real use case situations. Results obtained let us state that a soil-types recognition is not only possible but also accurate and reliable since overall median F-Score measures of 82% and 86% were obtained respectively with the Random Forest and the k-NN classifiers. Although the user independence of our system was not proven due to a limited number of involved users, the independence condition of the position of the wearable device was clearly demonstrated.