Standalone IoT Bioimpedance Device Supporting Real-Time Online Data Access

• Published in: IEEE internet of things journal Vol. 6; no. 6; pp. 9545 - 9554
• Main Authors: Vela, Luis M., Kwon, Hyeuknam, Rutkove, Seward B., Sanchez, Benjamin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bandwidths; Bioimpedance; Calibration; Change detection; Demodulation; Electrocardiography; Fast Fourier transformations; four-electrode technique; Fourier transforms; Frequency measurement; Impedance; Integrated circuits; Internet access; Internet of Things; Internet of Things (IoT) device; Local area networks; multisine excitations; Noise levels; Real time; Signal processing; Signal to noise ratio; Universal Serial Bus; Web browsers; Wireless networks
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2019.2929459

The use of electrical bioimpedance methods in medical and personalized healthcare applications requires sophisticated hardware and measurement settings. Here, we describe Zink, a standalone bioimpedance analyzer with Internet of Things (IoT) monitoring features. Zink can be connected to a secure wireless or local area network and accessed using any device with a Web browser and Internet access. Through a user-friendly Web-based access able to handle multiple simultaneous connections, the user(s) can perform single or multiple bioimpedance measurement(s) remotely. Zink supports the measurement of bioimpedance in both time and frequency domains using stepped-sine and multisine excitation signals. Embedded signal processing calculates bioimpedance from voltage and current signals using a coherent demodulation and the fast Fourier transform. Zink can also measure bioimpedance synchronized with either electrocardiogram (ECG) or electromyogram signals. The data is displayed on the user's Web client from where it can be downloaded for further analysis. Zink full bandwidth is 100 mHz to 10 MHz and the average signal-to-noise ratio tested from 1 kHz to 1 MHz is 56 dB. The extended calibration method proposed here reduces the average magnitude error with respect to the existing three-parameter calibration approach by 0.6 Ω (0.3%) measuring phantoms. Thoracic bioimpedance and ECG measurements on volunteers demonstrate the feasibility of detecting respiration changes while showing the results in real time on the user device's Web browser. The performance, portability, and integration of Zink makes it a suitable standalone platform for medical and personalized health IoT monitoring applications.