Research Interests

Wearable Healthcare Devices

Wearable Device Design and Integration:

Wearable system design and integration require interdisciplinary knowledge and teamwork. One needs to have a deep understanding of the market, the users, the technologies, and the regulations to develop a successful wearable device. We have extensive experience in wearable device development by assembling an interdisciplinary team consisting of chemical engineers, electrical engineers, mechanical engineers, and software engineers. We have successfully developed a wireless FeNO Analyzer for sensitive detection of ppb level nitric oxide in human breath for asthma management. The prototype and technology have been transferred to a pharmaceutical company for commercialization. We have also invented a Portable Metabolic Tracker (Breezing) and the world-first Wearable Metabolic Tracker (Breezing Med) by monitoring the oxygen consumption and carbon dioxide production in human breath. This wearable metabolic tracker has received FDA 510k clearance. Both Breezing and Breezing Med have been successfully transferred from research lab to marketplace. We are currently developing a wearable badge device for monitoring the personal chemical exposure of workers in manufacturing sites. The device can be worn by the workers during their working activities and their exposure to multiple chemical pollutants (including NO2, O3, HCHO, CO, SO2, and NH3) can be continuously and simultaneously monitored. This wearable badge device can be widely used to promote the safety of industrial workplaces and the health of workers.

Wearable-Based Data Science:

Wearable devices and their networks could capture and record continuous streams of health data about the patient. Making evidence-based clinical decisions requires the process of large amounts of data to create new insights, and build predictive models to guide personalized treatment. The scientific methods in data science offer effective tools for developing algorithms to extract knowledge and insights from the raw and unstructured data generated by wearables. We have developed a principle component analysis (PCA)-based data processing algorithm to extract respiration patterns from wearable mask devices. PCA is a statistical method that can simplify the complexity in high-dimensional data while retaining the key features and patterns. Since it is an effective method for dimensionality reduction and easy visualization of large data sets while maintaining the key information, the PCA is widely used in biological research, where high-dimensional data are very common. By implementing variables from the frequency domain, amplitude domain, hybrid domain, and waveform domain into the data processing algorithms, the unique respiration pattern of each patient has been successfully extracted and visualized. This data processing algorithm provides a new dimension for the screening, diagnosis, and management of asthma and chronic obstructive pulmonary disease (COPD).

Related Publications:

1. Vishal Varun Tipparaju, Di Wang, Jingjing Yu, Fang Chen, Francis Tsow, Erica Forzani, Nongjian Tao, and Xiaojun Xian, Respiration Pattern Recognition by Wearable Mask Device, Biosensors and Bioelectronics, 169, 112590 (2020)

2. Vishal Varun Tipparaju, Sabrina Jimena Mora, Jingjing Yu, Francis Tsow, Xiaojun Xian, Wearable Transcutaneous CO₂ Monitor Based on Miniaturized Nondispersive Infrared Sensor, IEEE Sensors Journal, 21, 17327-17334 (2021)

3. Vishal Varun Tipparaju, Kyle Mallires, Di Wang, Francis Tsow, and Xiaojun Xian, Mitigation of Data Packet Losses in Bluetooth Low Energy for Wearable Ecosystem, Biosensors, 11, 350 (2021)

4. Erica S. Forzani, Nongjian Tao, Xiaojun Xian, Francis Tsow, Mouthpiece For Accurate Detection Of Exhaled Nitric Oxide, US Patent, US 9,931,055 B2 (2018)

5. Francis Tsow, Xiaojun Xian, Erica S. Forzani, Nongjian Tao, Portable Metabolic Analyzer System, US Patent, US 10,078,074 B2 (2018)

6. Xiaojun Xian, Devon Bridgeman, Francis Tsow, Erica S. Forzani, Nongjian Tao, Self-Contained Wearable Metabolic Analyzer, International Patent, Application Number PCT/US19/55235 (2019)