Javier Espina

Plug 'n Play Simplicity for Wireless Medical Body Sensors

Wireless medical body sensors are a key technology for unobtrusive health monitoring. The easy setup of such wireless body area networks is crucial to protect the user from the complexity of these systems. But automatically forming a wireless network comprising all sensors attached to the same body is challenging. We present a method for making wireless body-worn medical sensors aware of the persons they belong to by combining body-coupled with wireless communication. This enables a user to create a wireless body sensor network by just sticking the sensors to her body. A personal identifier allows sensors to annotate their readings with a user ID thereby ensuring safety in personal healthcare environments with multiple users.

Network Topologies, Communication Protocols, and Standards

Every network has a topology that determines the way in which different devices of the network are arranged and how they communicate with each other. Here we need to distinguish between physical and logical topologies. The former refers to the physical layout of the network, i.e., the way that devices are physically connected to the network, either through actual cables or direct wireless communication links. By contrast, the logical topology of a network refers to the manner that data flows through the network from one node to the other without worrying about the physical interconnection of the devices for transporting a packet from a source to a destination device. The two lower layers of the Open Systems Interconnection (OSI) reference model (ISO/IEC international standard, Information technology – open systems interconnection – basic reference model: the basic model, 2nd edn, 1994) , the physical and data link layer, define the physical topology of a network, while the network layer is responsible for the logical topology.

BASUMA - the sixth sense for chronically ill patients

Continuous monitoring and analyzing of vital signs is the key for detecting at an early stage when a patients state of health changes to the worse, thereby preventing emergency cases, which are harmful to the patient and costly for the healthcare system. The BASUMA project is concerned with developing an energy-efficient and robust system-on-chip platform for wireless body sensors networks that enable health monitoring of chronically ill patients in their own homes. Initial application areas of BASUMA are: improving the treatment of chronic obstructive pulmonary disease patients and enhancing the ambulatory chemo therapy of women suffering from breast cancer

Wearable body sensor network towards continuous cuff-less blood pressure monitoring

We present a wearable IEEE 802.15.4-based Body Sensor Network (BSN) that enables continuous cuff-less blood pressure monitoring, opening up new perspectives for hypertension diagnosis and treatment, cardio-vascular event detection, and stress monitoring. Arterial blood pressure is estimated based on the Pulse Arrival Time (PAT), which is measured using a single lead electrocardiogram (ECG) patch on the chest and a photoplethysmogram (PPG) sensor at the finger or ear. Measurement context information-user posture and activity level-is extracted using a 3-D acceleration sensor. Since precise PAT measurements require the synchronization of the BSN devicespsila clocks, the Flooding Time Synchronization Protocol (FTSP) was implemented. The acquired data are stored and displayed on a PDA or a wristwatch. Our BSN can currently operate for up to eight hours and perform PAT measurements under moderate activity conditions. Future work includes higher motion tolerance, posture-corrected blood pressure estimation and on-sensor data processing and storage.

Bed exit prediction based on movement and posture data

Falls in nursing homes and hospitals take often place immediately after a bed exit of a patient. An alarm signaling the exit from the bed may already be too late for staff to react. In this paper we explore the possibilities of detecting the sequences of preparatory movements before the bed exit and in this way create an early warning of the preparation of bed exit. The method is described and tested using annotated accelerometer data collected from volunteers. A plausibility assessment is also done by comparing accelerometer data from hospital patients with the output of a bed alarm system. It is demonstrated that the proposed method is able to detect a bed exit already seconds before the patient actually leaves the bed.

Practical comparison of ranging in IEEE 802.15.4 and IEEE 802.15.4a medical body sensor networks

In this paper, a practical comparison of the wireless standards IEEE 802.15.4 and IEEE 802.15.4a is described. Particularly the ranging capabilities of both systems are studied, with the aim of enabling a senior health monitoring application to automatically detect with which user sensors are associated. The relevant characteristics of both systems are presented, as well as the difference between received signal strength and time of arrival based ranging methods. Next, a measurement campaign based on a TI 802.15.4/Zigbee chipset and IMECs 802.15.4a demonstrator is presented. Results show that the received signal strength is too heavily influenced by other parameters to use it as a metric for accurate distance estimation. IEEE 802.15.4a based time of arrival methods are far more accurate. Particularly leading edge detection performs well, with an average error in line-of-sight conditions below 6 cm.