Human Activity Recognition (HAR) is currently recognized as a key element of a more general framework designed to perform continuous monitoring of human behaviors in the area of Ambient Assisted Living (AAL), well-being management, medical diagnosis, elderly care, rehabilitation, entertainment and surveillance in smart home environments. In this paper, an innovative HAR system, exploiting the potential of wearable devices integrated with the skills of deep learning techniques, is presented with the aim of recognizing the most common daily activities of a person at home. The designed wearable sensor embeds an Inertial Measurement Unit (IMU) and a Wi-Fi section to send data on a cloud service and to allow direct connection to the Internet through a common home router so that the user themselves could manage the installation procedure. The sensor is coupled to a CNN network designed to make inferences with the minimum possible resources to keep open the way of its implementation on low-cost or embedded devices. The system is conceived for daily activity monitor and nine different activities can be highlighted with an accuracy of 97%.

Location-based services have increased in popularity in recent years and can be fruitfully exploited in the field of smart homes, opening the doors to a wide range of personalized services. In this context, radio technology can be widely employed since, other than connecting devices in the home system, it offers solutions for the user localization issue without the need of any extra device. Techniques based on received signal strength indicator (RSSI) are often used, relying on fingerprinting or proximity algorithms. In this paper, a novel RSSI-based fingerprinting approach for room-level localization is presented: it is a threshold algorithm based on receiver operating characteristic analysis. Moreover, the actual user location is estimated from his/her interaction with the home system devices deployed in the house: if the home environment is inhabited by more than one person, it becomes of utmost importance the identification of who is actually interacting with a given device. A proximity method is exploited for this purpose. Tests have been carried out to characterize the approach, particularly, the effects of RSSI samples, number and position, of the anchor nodes have been analyzed. Finally, some considerations about power consumption of the mobile node have been presented.

The measurement of the analyte concentration in electrochemical biosensors traditionally requires costly laboratory equipment to obtain accurate results. Innovative portable solutions have recently proposed, but usually they lean on PC or smartphones for data elaboration and they exhibit poor resolution or portability and proprietary software. This paper presents a low-cost portable system, assembling an ad-hoc designed analog front-end and a development board equipped with a system-on-chip integrating a microcontroller and a Wi-Fi network processor. The wireless module enables the transmission of measurements directly to a cloud service for sharing device outcome with users (physicians, caregivers, etc.). In doing so, the system does not require customized software nor other devices involved in data acquisition. Further, when any internet connection is lost, the data are stored on board for subsequent transmission when a Wi-Fi connection is available. The noise output voltage spectrum has been characterized. Since the designed device is intended to be battery-powered to enhance portability, investigations about battery lifetime were carried out. Finally, data acquired with a conventional benchtop Autolab PGSTAT-204 electrochemical workstation are compared with the outcome of our developed device to validate the effectiveness of our proposal. To this end, we selected ferri/ferrocyanide as redox probe, obtaining the calibration curves for both platforms. The final outcomes are shown to be feasible, accurate and repeatable.

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.

Nowadays, lithium-ion is among the most used chemistry for batteries and shows an increasing market growth rate. However, to reduce failure or safety risks, the battery State-of-Charge (SoC) must be accurately monitored and predicted by a suitable Battery Management System (BMS). Artificial Intelligence (AI) techniques have been extensively applied to this field with good results. Typically, AIs are trained on dynamic profile data, emulating battery charging and discharging cycles related to the application under test. In this paper, a novel approach is presented: application independent constant current profiles are used to train a Support Vector Regression (SVR) algorithm. To enhance the estimation accuracy, the output of the obtained SVR model was post-processed. Finally, an error correction algorithm was applied to further reduce the estimation error. The system is validated over test cycles representing different application scenarios for the battery cell operations. For the development of the proposed approach, a total of 105 constant current discharge profiles for the training and 20 realistic test cycles for the validation have been considered, including standard automotive cycles and a generic battery powered power tool. The performance in the SoC estimation resulted in a Root Mean Square Error (RMSE) of 0.94% and a Mean Absolute Error (MAE) of 0.75% over all the test cycles. Error metrics are comparable to those obtained for SoC estimation AI algorithms based on traditional approaches using application dependent battery profiles for the training phase.

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.