Elisabetta Farella

Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection

Activity recognition from an on-body sensor network enables context-aware applications in wearable computing. A guaranteed classification accuracy is desirable while optimizing power consumption to ensure the systems wearability. In this paper, we investigate the benefits of dynamic sensor selection in order to use efficiently available energy while achieving a desired activity recognition accuracy. For this purpose we introduce and characterize an activity recognition method with an underlying run-time sensor selection scheme. The system relies on a meta-classifier that fuses the information of classifiers operating on individual sensors. Sensors are selected according to their contribution to classification accuracy as assessed during system training. We test this system by recognizing manipulative activities of assembly-line workers in a car production environment. Results show that the systems lifetime can be significantly extended while keeping high recognition accuracies. We discuss how this approach can be implemented in a dynamic sensor network by using the context-recognition framework Titan that we are developing for dynamic and heterogeneous sensor networks.

Tracking Motion Direction and Distance With Pyroelectric IR Sensors

Passive IR (PIR) sensors are excellent devices for wireless sensor networks (WSN), being low-cost, low-power, and presenting a small form factor. PIR sensors are widely used as a simple, but reliable, presence trigger for alarms, and automatic lighting systems. However, the output of a PIR sensor depends on several aspects beyond simple people presence, as, e.g., distance of the body from the sensor, direction of movement, and presence of multiple people. In this paper, we present a feature extraction and sensor fusion technique that exploits a set of wireless nodes equipped with PIR sensors to track people moving in a hallway. Our approach has reduced computational and memory requirements, thus it is well suited for digital systems with limited resources, such as those available in sensor nodes. Using the proposed techniques, we were able to achieve 100% correct detection of direction of movement and 83.49%-95.35% correct detection of distance intervals.

Wireless sensor networks: Enabling technology for ambient intelligence

Wireless sensor networks are one of the most rapidly evolving research and development fields for microelectronics. Their applications are countless, and the market potentials are huge. However, many technical hurdles have to be overcome to achieve a widespread diffusion of wireless sensor network technology. This paper summarizes the trends of evolution in wireless sensor network nodes, focusing on hardware architectures and fabrication technology. We describe four generations of sensor networks (obtrusive, parasitic, symbiotic and bio-inspired), moving from the recent past to the future. We outline the key research challenges and the common themes in the field. and development fields for microelectronics. Their applications are countless, and the market potentials are huge. However, many technical hurdles have to be overcome to achieve a widespread diffusion of wireless sensor network technology. This paper summarizes the trends of evolution in wireless sensor network nodes, focusing on hardware architectures and fabrication technology. We describe four generations of sensor networks (obtrusive, parasitic, symbiotic and bio-inspired), moving from the recent past to the future. We outline the key research challenges and the common themes in the field. and development fields for microelectronics. Their applications are countless, and the market potentials are huge. However, many technical hurdles have to be overcome to achieve a widespread diffusion of wireless sensor network technology. This paper summarizes the trends of evolution in wireless sensor network nodes, focusing on hardware architectures and fabrication technology. We describe four generations of sensor networks (obtrusive, parasitic, symbiotic and bio-inspired), moving from the recent past to the future. We outline the key research challenges and the common themes in the field. and development fields for microelectronics. Their applications are countless, and the market potentials are huge. However, many technical hurdles have to be overcome to achieve a widespread diffusion of wireless sensor network technology. This paper summarizes the trends of evolution in wireless sensor network nodes, focusing on hardware architectures and fabrication technology. We describe four generations of sensor networks (obtrusive, parasitic, symbiotic and bio-inspired), moving from the recent past to the future. We outline the key research challenges and the common themes in the field. and development fields for microelectronics. Their applications are countless, and the market potentials are huge. However, many technical hurdles have to be overcome to achieve a widespread diffusion of wireless sensor network technology. This paper summarizes the trends of evolution in wireless sensor network nodes, focusing on hardware architectures and fabrication technology. We describe four generations of sensor networks (obtrusive, parasitic, symbiotic and bio-inspired), moving from the recent past to the future. We outline the key research challenges and the common themes in the field. and development fields for microelectronics. Their applications are countless, and the market potentials are huge. However, many technical hurdles have to be overcome to achieve a widespread diffusion of wireless sensor network technology. This paper summarizes the trends of evolution in wireless sensor network nodes, focusing on hardware architectures and fabrication technology. We describe four generations of sensor networks (obtrusive, parasitic, symbiotic and bio-inspired), moving from the recent past to the future. We outline the key research challenges and the common themes in the field. and development fields for microelectronics. Their applications are countless, and the market potentials are huge. However, many technical hurdles have to be overcome to achieve a widespread diffusion of wireless sensor network technology. This paper summarizes the trends of evolution in wireless sensor network nodes, focusing on hardware architectures and fabrication technology. We describe four generations of sensor networks (obtrusive, parasitic, symbiotic and bio-inspired), moving from the recent past to the future. We outline the key research challenges and the common themes in the field.

Activity recognition from on-body sensors by classifier fusion: sensor scalability and robustness

Activity recognition from on-body sensors is affected by sensor degradation, interconnections failures, and jitter in sensor placement and orientation. We investigate how this may be balanced by exploiting redundant sensors distributed on the body. We recognize activities by a meta-classifier that fuses the information of simple classifiers operating on individual sensors. We investigate the robustness to faults and sensor scalability which follows from classifier fusion. We compare a reference majority voting and a naive Bayesian fusion scheme. We validate this approach by recognizing a set of 10 activities carried out by workers in the quality assurance checkpoint of a car assembly line. Results show that classification accuracy greatly increases with additional sensors (50% with 1 sensor, 80% and 98% with 3 and 57 sensors), and that sensor fusion implicitly allows to compensate for typical faults up to high fault rates. These results highlight the benefit of large on- body sensor network rather than a minimum set of sensors for activity recognition and prompts further investigation.

A Wireless Body Area Sensor Network for Posture Detection

Body Area Sensor Networks (BASN) are an emerging technology enabling the design of natural Human Computer Interfaces (HCI) in the context of Ambient Intelligence. This class of interactive applications poses new challenges on sensor network design that are hard to be faced using traditional solutions optimized for environmental monitoringlike applications. In this paper we present a novel solution for wireless and wearable posture recognition based on a custom-designed wireless body area sensor network, called WiMoCA. Nodes of the network, mounted on different parts of the human body, exploit tri-axial accelerometers to detect body postures. Afterwards we discuss results of interactive performance and power consumption optimizations required to match application constraints.

Bluetooth indoor localization with multiple neural networks

Over the last years, many different methods have been proposed for indoor localization and navigation services based on Radio frequency (RF) technology and Radio Signal Strength Indicator (RSSI). The accuracy achieved with such systems is typically low, mainly due to the variability of RSSI values, unsuitable for classic localization methods (e.g. triangulation). In this paper, we propose a novel approach based on multiple neural networks. We demonstrate with experimental results that by training and then activating different neural networks, tailored on the user orientation, high definition accuracy is achievable, allowing indoor navigation with a cost effective Bluetooth (BT) architecture.

Interfacing human and computer with wireless body area sensor networks: the WiMoCA solution

Wireless Body Area Sensor Networks (WBASN) are an emerging technology enabling the design of natural human–computer interfaces (HCI). Automatic recognition of human motion, gestures, and activities is studied in several contexts. For example, mobile computing technology is being considered as a replacement of traditional input systems. Moreover, body posture and activity monitoring can be used for entertainment and health-care applications. However, until now, little work has been done to develop flexible and efficient WBASN solutions suitable for a wide range of applications. Their requirements pose new challenges for sensor network designs, such as optimizing traditional solutions for use as environmental monitoring-like applications and developing on-the-field stress tests. In this paper, we demonstrate the flexibility of a custom-designed WBASN called WiMoCA with respect to a wide range of posture and activity recognition applications by means of practical implementation and on-the-field testing. Nodes of the network mounted on different parts of the human body exploit tri-axial accelerometers to detect its movements. The advanced digital Micro-electro-mechanical system (MEMS) based inertial sensor has been chosen for WiMoCA because it demonstrated high flexibility of use in many different situations, providing the chance to exploit both static and dynamic acceleration components for different purposes. Furthermore, the sensibility and accuracy of the sensing element is perfectly adequate for monitoring human movement, while keeping cost low and size compact, thus meeting our requirements. We implemented three types of applications, stressing the WBASN in many aspects. In fact, they are characterized by different requirements in terms of accuracy, timeliness, and computation distributed on sensing nodes. For each application, we describe its implementation, and we discuss results about performance and power consumption.

An integrated multi-modal sensor network for video surveillance

To enhance video surveillance systems, multi-modal sensor integration can be a successful strategy. In this work, a computer vision system able to detect and track people from multiple cameras is integrated with a wireless sensor network mounting PIR (Passive InfraRed) sensors. The two subsystems are briefly described and possible cases in which computer vision algorithms are likely to fail are discussed. Then, simple but reliable outputs from the PIR sensor nodes are exploited to improve the accuracy of the vision system. In particular, two case studies are reported: the first uses the presence detection of PIR sensors to disambiguate between an opened door and a moving person, while the second handles motion direction changes during occlusions. Preliminary results are reported and demonstrate the usefulness of the integration of the two subsystems.

A Wearable System for Gait Training in Subjects with Parkinson’s Disease

In this paper, a system for gait training and rehabilitation for Parkinsons disease (PD) patients in a daily life setting is presented. It is based on a wearable architecture aimed at the provision of real-time auditory feedback. Recent studies have, in fact, shown that PD patients can receive benefit from a motor therapy based on auditory cueing and feedback, as happens in traditional rehabilitation contexts with verbal instructions given by clinical operators. To this extent, a system based on a wireless body sensor network and a smartphone has been developed. The system enables real-time extraction of gait spatio-temporal features and their comparison with a patients reference walking parameters captured in the lab under clinical operator supervision. Feedback is returned to the user in form of vocal messages, encouraging the user to keep her/his walking behavior or to correct it. This paper describes the overall concept, the proposed usage scenario and the parameters estimated for the gait analysis. It also presents, in detail, the hardware-software architecture of the system and the evaluation of system reliability by testing it on a few subjects.

Enhancing the spatial resolution of presence detection in a PIR based wireless surveillance network

Pyroelectric sensors are low-cost, low-power small components commonly used only to trigger alarm in presence of humans or moving objects. However, the use of an array of pyroelectric sensors can lead to extraction of more features such as direction of movements, speed, number of people and other characteristics. In this work a low-cost pyroelectric infrared sensor based wireless network is set up to be used for tracking people motion. A novel technique is proposed to distinguish the direction of movement and the number of people passing. The approach has low computational requirements, therefore it is well-suited to limited-resources devices such as wireless nodes. Tests performed gave promising results.