Bluetooth Medical Sensor Node — Biomedical Instrumentation & Clinical Sensing

Abstract — Bluetooth Medical Sensor Node

so, the exchanged signals can be computed with lower dimensions between nodes, where the optimal LCMV beamformer is still available at each node as if each node is to transmit its all raw sensor signal observations. The proposed beamformer is fully distributable without imposing restrictions on the underlying network topology or scaling computa- tional complexity, i.e., there is no increase in the per-node complexity when new nodes are added to the networks. Compared with state-of-the-art distributed node-speciIc algorithms that are often time-recursive, the proposed beamformer exactly solves the LCMV beamformer optimally frame by frame, which has much lower computational complexity and is more robust to acoustic transfer func- tion estimation error and voice activity detector error. Numerous experimental results are presented to validate the eIectiveness of the proposed beamformer.

Sensor Interfacing & Analog Front-End Conditioning

distributed beamforming, node-speciIc, speech enhancement, wireless acoustic sensor networks. Wireless acoustic sensor networks (WASNs) generally consist of several nodes, where each node has one or many sensors, a processing unit, and a wireless communication module allowing them to exchange data. Compared with the traditional and single sensor array , WASNs can physically cover a wider area, which have more opportunity to select a subset of nodes close to some target sources, and thus higher signal-to-noise ratio (SNR) and direct-to-reverberant ratio (DRR) can be expected [2, 3]. As the next-generation technology for au- dio acquisition and processing, WASNs have many potential applications, such as binaural hearing aids [4, 5, 6], (hands-free) speech communication systems [7, 8, 9], and acoustic monitoring systems [10, 11, 12, 13].

Bio-Signal Waveform Analysis & Simulation Findings

In principle, all the sensor signal observations from diIerent nodes can be transmitted to a fusion center, and then an optimal beamformer can be com- puted, where this approach is known as the centralized estimation [14, 15, 16]. The centralized estimation requires a large communication bandwidth, a large transmission power consumption at the individual nodes, and a nonnegligible computational complexity at the fusion center. However, both the power and the communication bandwidth resources in WASNs are often limited. Further- more, in many WASNs applications, the fusion center may be undesirable due to privacy considerations . A trivial solution is obtained by only utilizing the local sensor signal observations at a single node without any communication link with other nodes. Whereas, this solution cannot utilize the entire informa- tion from the WASNs and hence is only sub-optimal. A promising solution is to develop a suitable distributed approach, which often has three stages . At the Irst stage, each node processes its own sensor signal observations to obtain some compressed signals. At the second stage, only these compressed signals are transmitted to reduce the communication bandwidth. At the last stage, a target signal is obtained by merging all these compressed signals properly. Distributed speech enhancement algorithms can be roughly divided into two main categories: node-speciIc and non-node-speciIc. For the node-speciIc es- timation algorithms, each node in the WASNs can estimate a diIerent target signal, that is to say, a target source for one node may be an interfering source for another node, and vice versa. The node-speciIc estimation problem is intrinsic in a blind beamforming framework where the acoustic transfer functions (ATFs) between the target sound sources and the sensors are generally unknown. For these blind beamformers, some subspace estimation algorithms can be used to estimate the subspace of the ATFs [19, 20], and then the target signal can be estimated as it is observed at a reference sensor. If each node in the WASNs chooses its own local sensor as reference, the spatial information of the target source can be preserved.

Scope & Clinical Applications

• Real-time multi-parameter vital signs telemetry (ECG, Heart Rate, SpO2, Temperature, Respiration)
• Low-noise Analog Front-End (AFE) conditioning with active Driven Right Leg (DRL) 50 Hz cancellation
• Digital signal processing in MATLAB: Pan-Tompkins QRS detection, FIR/IIR notch, and Continuous Wavelet Transforms
• Finite element tissue bio-electromagnetics and orthopedic mechanical simulations in COMSOL & ANSYS
• Complete Arduino firmware .INO sketches, circuit schematics, and university dissertation reports