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.

This paper presents a text independent speaker authentication method adapted to mobile devices. Special attention was placed on delivering a fully operational application, which admits a sufficient reliability level and an efficient functioning. To this end, we have excluded the need for any network communication. Hence, we opted for the completion of both the training and the identification processes directly on the mobile device through the extraction of linear prediction cepstral coefficients and the naive Bayes algorithm as the classifier. Furthermore, the authentication decision is enhanced to overcome misidentification through access privileges that the user should attribute to each application beforehand. To evaluate the proposed authentication system, eleven participants were involved in the experiment, conducted in quiet and noisy environments. Public speech corpora were also employed to compare this implementation to existing methods. Results were efficient regarding mobile resources’ consumption. The overall classification performance obtained was accurate with a small number of samples. Then, it appeared that our authentication system might be used as a first security layer, but also as part of a multilayer authentication, or as a fall-back mechanism.

Several smart home architecture implementations have been proposed in the last decade. These architectures are mostly deployed in laboratories or inside real habitations built for research purposes to enable the use of ambient intelligence using a wide variety of sensors, actuators and machine learning algorithms. However, the major issues for most related smart home architectures are their price, proprietary hardware requirements and the need for highly specialized personnel to deploy such systems. To tackle these challenges, lighter forms of smart home architectures known as smart homes in a box (SHiB) have been proposed. While SHiB remain an encouraging first step towards lightweight yet affordable solutions, they still suffer from few drawbacks. Indeed, some of these kits lack hardware support for some technologies, and others do not include enough sensors and actuators to cover most smart homes’ requirements. Thus, this paper introduces the LIARA Portable Smart Home Kit (LIPSHOK). It has been designed to provide an affordable SHiB solution that anyone is able to install in an existing home. Moreover, LIPSHOK is a generic kit that includes a total of four specialized sensor modules that were introduced independently, as our laboratory has been working on their development over the last few years. This paper first provides a summary of each of these modules and their respective benefits within a smart home context. Then, it mainly focus on the introduction of the LIPSHOK architecture that provides a framework to unify the use of the proposed sensors thanks to a common modular infrastructure capable of managing heterogeneous technologies. Finally, we compare our work to the existing SHiB kit solutions and outline that it offers a more affordable, extensible and scalable solution whose resources are distributed under an open-source license.

In a previous work, we presented a method to recognize different soil types based on inertial data generated by a user's gait through a wearable device. Although results we have obtained were encouraging, we judged that this method needed further evaluation. Thus, this paper aims at comparing our previous approach between several acquisition methods. The device was upgraded to a 9-axis IMU and a comparison of this wearable with a mobile phone was also offered. Both the features processing and the classification phases remain unchanged from our previous work to perform a proper comparison. Although this evaluation let us expose a slight improvement in the recognition rate, it allowed us to prove the reliability of our device since the performance obtained with the mobile phone was similar.

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.