Marco Bassoli

Recent research in wearable sensors have led to the development of an advanced platform capable of embedding complex algorithms such as machine learning algorithms, which are known to usually be resource-demanding. To address the need for high computational power, one solution is to design custom hardware platforms dedicated to the specific application by exploiting, for example, Field Programmable Gate Array (FPGA). Recently, model-based techniques and automatic code generation have been introduced in FPGA design. In this paper, a new model-based floating-point accumulation circuit is presented. The architecture is based on the state-of-the-art delayed buffering algorithm. This circuit was conceived to be exploited in order to compute the kernel function of a support vector machine. The implementation of the proposed model was carried out in Simulink, and simulation results showed that it had better performance in terms of speed and occupied area when compared to other solutions. To better evaluate its figure, a practical case of a polynomial kernel function was considered. Simulink and VHDL post-implementation timing simulations and measurements on FPGA confirmed the good results of the stand-alone accumulator.

Reed–Solomon (RS) codes are one of the most used solutions for error correction logic in data communications. RS decoders are composed of several blocks: among them, many efforts have been made to optimize the error magnitude evaluation module. This paper aims to assess the performance of an innovative algorithm introduced in the literature by Lu et al. under different systems configurations and hardware platforms. Several configurations of the encoded message chosen between those typically used in different applications have been designed to be run on an FPGA (field programmable gate array) device and an MCU (microcontroller unit). The performances have been evaluated in terms of resource usage and output delay for the FPGA and in terms of code execution time for the MCU. As a benchmark in the analysis, the well-established Forney’s method is exploited: it has been implemented in the same configurations and on the same hardware platforms for a proper comparison. The results show that the theoretical finding are fully confirmed only in the MCU implementation, while on FPGA, the choice of one method with respect to the other depends on the optimization feature (i.e., time or area) that has been decided as a preference in the specific application.

The trend toward technology ubiquity in human life is constantly increasing and the same tendency is clear in all technologies aimed at human monitoring. In this framework, several smart home system architectures have been presented in literature, realized by combining sensors, home servers, and online platforms. In this paper, a new system architecture suitable for human monitoring based on Wi-Fi connectivity is introduced. The proposed solution lowers costs and implementation burden by using the Internet connection that leans on standard home modem-routers, already present normally in the homes, and reducing the need for range extenders thanks to the long range of the Wi-Fi signal. Since the main drawback of the Wi-Fi implementation is the high energy drain, low power design strategies have been considered to provide each battery-powered sensor with a lifetime suitable for a consumer application. Moreover, in order to consider the higher consumption arising in the case of the Wi-Fi/Internet connectivity loss, dedicated operating cycles have been introduced obtaining an energy savings of up to 91%. Performance was evaluated: in order to validate the use of the system as a hardware platform for behavioral services, an activity profile of a user for two months in a real context has been extracted.

Among sensible goals of active and assisted living paradigm is the unobtrusive monitoring of daily living activities. Based on such monitoring, anomalies and trends can be discovered, which possibly allows for early assessment of health issues and for prevention policies. However, when dealing with the home environment, and especially with older adults, obtrusiveness, usability, and cost concerns are of the utmost relevance. Smart objects can be designed to this purpose and deployed into the home: they usually feature low data rates and are customarily implemented by relying on conventional wireless sensor network approaches (ZigBee, Z-Wave, etc). This, however, results in “ad hoc” home networking, which is somehow obtrusive, complicated, and possibly expensive. In this paper, we discuss the implementation of behavioral sensors based on the familiar and ubiquitous Wi-Fi technology, suitable for a “plug-and-play” deployment. Sensors are connected to a cloud platform, embodying a genuine Internet of Things approach. With respect to conventional approaches, much better scalability, flexibility, and inexpensiveness can be attained. The main expected drawback comes from the higher power consumption, inherently needed to sustain much higher data rates. This paper focuses on such an issue, illustrating design techniques aimed at optimizing power consumption and battery lifetime. Performance results are shown, which definitely fall within a practical range and are fully comparable with more conventional approaches.

Human monitoring is important in a wide range of applications and, most notably, in health management. Within the Active and Assisted Living (AAL) framework, human monitoring techniques have particular relevance: among AAL purposes is that of applying ambient intelligence paradigm to enable older adults or people with specific demands to live longer and independently in their homes, reducing the need of institutionalization. Sensors are used to track some features of daily living activities, which are then processed to infer health-relevant information. When dealing with the home environment, cost, intrusiveness and ease of installation and management are of the utmost importance: wireless sensors are therefore often used, and cloud-based service can be exploited to reduce the need for home-based hardware devices, this matching the increasingly widespread Internet of Things (IoT) paradigm. Wireless sensor connectivity may rely upon different standards and protocols: most frequently, WSN (wireless sensor network) dedicated protocols are exploited (e.g. ZigBee, Z-Wave) taking advantage of optimization toward low data-rate, low power features. However, ad-hoc deployment of such networks is required, which may result in expensive and burdening installation and management tasks. In this paper, we present a complete Wi-Fi based solution (named CARDEA Wi-Fi), in which sensors specifically designed for human monitoring purposes are straightforwardly connected to the internet cloud by exploiting the Wi-Fi communication protocol (universally diffused for home connectivity), without the need of local gateway devices (except for a Wi-Fi modem router). This results in lightweight installation and management procedures, leading to a “plug and play” approach, which is particularly appealing when interacting with persons having low or no technical skill. The CARDEA Wi-Fi architecture is discussed, and the implementation of a set of “behavioral” sensors is described. Preliminary figures about performance are also given, with particular reference to battery lifetime.

Continuous monitoring of daily life activities within the home environment is among most appealing and challenging goals of technologies supporting the Active and Assisted Living paradigm. The HELICOPTER (AAL-JP) project aims at merging conventional telemedicine and home monitoring into a unique, holistic framework. To this purpose, a network of heterogeneous sensors is deployed into the user's home: raw data coming from sensors should then be gathered and analyzed to extract meaningful information, possibly related to user's health and wellness. Besides the inherent complexity of designing and deploying a suitable infrastructure, intrusiveness, usability and acceptability concern come into play. In this paper, we introduce the overall project vision, focusing mostly on the infrastructure implementation and discussing the extension of the monitoring approach to a multi-user environment. In fact, if two or more persons live in the same environment, data coming from home sensors need disambiguation, i.e., the activity detected by a given sensor need to be ascribed to a specific user, in order to contribute to the appropriate behavioral profile. We propose a simple approach, based on fusion of data coming from wearable and home devices. We discuss lab tests, showing good accuracy in “action tagging”, and illustrate practical results coming from actual pilots, involving older end-users in different European countries.