Antonio Ridi

School of Engineering and Architecture of Fribourg · Informatic

The automatic identification of appliances through the analysis of their electricity consumption has several purposes in Smart Buildings including better understanding of the energy consumption, appliance maintenance and indirect observation of human activities. Electric signatures are typically acquired with IoT smart plugs integrated or added to...

We asset about the analysis of electrical appliance consumption signatures for the identification task. We apply Hidden Markov Models to appliance signatures for the identification of their category and of the most probable sequence of states. The electrical signatures are measured at low frequency (10-1 Hz) and are sourced from a specific database...

We present ACS-F2, a new electric consumption signature database acquired from domestic appliances. The scenario of use is appliance identification with emerging applications such as domestic electricity consumption understanding, load shedding management and indirect human activity moni-toring. The novelty of our work is to use low-end electricity...

Diagnosing the glaucoma is a very difficult task for healthcare professionals. High intraocular pressure (IOP) remains the main treatable symptom of this degenerative disease which leads to blindness. Nowadays, new types of wearable sensors, such as the contact lens sensor Triggerfish®, provide an automated recording of 24-hour profile of ocular di...

Electricity load monitoring of appliances has become an important task considering the recent economic and ecological trends. In this game, machine learning has an important part to play, allowing for energy consumption understanding, critical equipment monitoring and even human activity recognition. This paper provides a survey of current research...

Internet-of-Things (IoT) devices, especially sensors are producing large quantities of data that can be used for gathering knowledge. In this field, machine learning technologies are increasingly used to build versatile data-driven models. In this paper, we present a novel architecture able to execute machine learning algorithms within the sensor n...

We assess the feasibility of unseen appliance recognition through the analysis of their electrical signatures recorded using low-cost smart plugs. By unseen, we stress that our approach focuses on the identification of appliances that are of different brands or models than the one in training phase. We follow a strictly defined protocol in order to...

In the last years, gesture recognition has gained increased attention in Human-Computer Interaction community. However, gesture segmentation, which is one of the most challenging tasks in gesture recognition applications, is still an open issue. Gesture segmentation has two main objectives: first, detecting when a gesture begins and ends; second, r...

This paper presents a wearable system based on kinesiologic electromyography that recognizes the user activity in real time. In particular, the system recognizes the following five activities: "walking", "running", "cycling", "sitting" and "standing". We conducted a study in order to select the opportune muscles and sensors placement. Furthermore,...

We report on the evaluation of signal processing and classification algorithms to automatically recognize electric appliances. The system is based on low-cost smart-plugs measuring periodically the electricity values and producing time series of measurements that are specific to the appliance consumptions. In a similar way as for biometric applicat...

We report on the creation of a database of appliance consumption signatures and two test protocols to be used for appliance recognition tasks. By means of plug-based low-end sensors measuring the electrical consumption at low frequency, typically every 10 seconds, we made two acquisition sessions of one hour on about 100 home appliances divided int...

We present the optimization of a wearable surface electromyography-based system for activity recognition in relation with the number of sensed muscles. The muscles of interest were four: Gastrocnemius, Tibialis Anterior, Vastus Lateralis and Erector Spinae. In particular, the system has been tested for the recognition of five everyday activities: "...

In this paper we propose an approach to address the gesture segmentation issue, an important concern strongly related to the gesture recognition field. Gesture segmentation has two main goals: first, detecting when a gesture begins and ends, second, understanding whether a gesture is meant to be meaningful for the machine or is a non-command gestur...