BACKGROUND: The accuracy of movement determination software in current activity trackers is insufficient for scientific applications, which are also not open-source.
OBJECTIVE: To address this issue, we developed an accurate, trainable, and open-source smartphone-based activity-tracking toolbox that consists of an Android app (HumanActivityRecorder) and 2 different deep learning algorithms that can be adapted to new behaviors.
METHODS: We employed a semisupervised deep learning approach to identify the different classes of activity based on accelerometry and gyroscope data, using both our own data and open competition data.
RESULTS: Our approach is robust against variation in sampling rate and sensor dimensional input and achieved an accuracy of around 87% in classifying 6 different behaviors on both our own recorded data and the MotionSense data. However, if the dimension-adaptive neural architecture model is tested on our own data, the accuracy drops to 26%, which demonstrates the superiority of our algorithm, which performs at 63% on the MotionSense data used to train the dimension-adaptive neural architecture model.
CONCLUSIONS: HumanActivityRecorder is a versatile, retrainable, open-source, and accurate toolbox that is continually tested on new data. This enables researchers to adapt to the behavior being measured and achieve repeatability in scientific studies.

Chemoresistive nanostructured gas sensors are employed in many diverse applications in the medical, industrial, environmental, etc. fields; therefore, it is crucial to have a device that is able to quickly calibrate and characterize them. To this aim, a portable, user-friendly device designed to easily calibrate a sensor in laboratory and/or on field is introduced here. The device comprises a small hermetically sealed chamber (containing the sensor socket and a temperature/humidity sensor), a pneumatic system, and a custom electronics controlled by a Raspberry Pi 4 developing board, running a custom software (Version 1.0) whose user interface is accessed via a multitouch-screen. This device automatically characterizes the sensor heater in order to precisely set the desired working temperature, it acquires and plots the sensor current-to-voltage and Arrhenius relationships on the touch screen, and it can record the sensor responses to different gases and environments. These tests were performed in dry air on two representative sensors based on widely used SnO2 material. The device demonstrated the independence of the Arrhenius plot from the film applied voltage and the linearity of the I-Vs, which resulted from the voltage step length (1-30 min) and temperature (200-550 °C).

A sensor device based on doped-carbon quantum dots is proposed herein for detection of nitrite in meat products by fluorescence quenching. For the sensing platform, carbon quantum dots doped with boron and functionalized with nitrogen (B,N-Cdot) were synthesized with an excellent 44.3% quantum yield via a one-step hydrothermal route using citric acid, boric acid, and branched polyethylenimine as carbon, boron, and nitrogen sources, respectively. After investigation of their chemical structure and fluorescent properties, the B,N-Cdot at aqueous suspensions showed high selectivity for NO2- in a linear range from 20 to 50 mmol L-1 under optimum conditions at pH 7.4 and a 340 nm excitation. Furthermore, the prepared B,N-Cdots successfully detected NO2- in a real meat sample with recovery of 91.4-104% within the analyzed range. In this manner, a B,N-Cdot/PVA nanocomposite film with blue emission under excitation at 360 nm was prepared, and a first assay detection of NO2- in meat products was tested using a smartphone application. The potential application of the newly developed sensing device containing a highly fluorescent probe should aid in the development of a rapid and inexpensive strategy for NO2- detection.

A fully integrated, flexible, and functional sensing device for exhaled breath analysis drastically transforms conventional medical diagnosis to non-invasive, low-cost, real-time, and personalized health care. 2D materials based on MXenes offer multiple advantages for accurately detecting various breath biomarkers compared to conventional semiconducting oxides. High surface sensitivity, large surface-to-weight ratio, room temperature detection, and easy-to-assemble structures are vital parameters for such sensing devices in which MXenes have demonstrated all these properties both experimentally and theoretically. So far, MXenes-based flexible sensor is successfully fabricated at a lab-scale and is predicted to be translated into clinical practice within the next few years. This review presents a potential application of MXenes as emerging materials for flexible and wearable sensor devices. The biomarkers from exhaled breath are described first, with emphasis on metabolic processes and diseases indicated by abnormal biomarkers. Then, biomarkers sensing performances provided by MXenes families and the enhancement strategies are discussed. The method of fabrications toward MXenes integration into various flexible substrates is summarized. Finally, the fundamental challenges and prospects, including portable integration with Internet-of-Thing (IoT) and Artificial Intelligence (AI), are addressed to realize marketization.

Production-scale fermentation processes in industrial biotechnology experience gradients in process variables, such as dissolved gases, pH and substrate concentrations, which can potentially affect the production organism and therefore the yield and profitability of the processes. However, the extent of the heterogeneity is unclear, as it is currently a challenge at large scale to obtain representative measurements from different zones of the reactor volume. Computational fluid dynamics (CFD) models have proven to be a valuable tool for better understanding the environment inside bioreactors. Without detailed measurements to support the CFD predictions, the validity of CFD models is debatable. A promising technology to obtain such measurements from different zones in the bioreactors are flow-following sensor devices, whose development has recently benefitted from advancements in microelectronics and sensor technology. This paper presents the state of the art within flow-following sensor device technology and addresses how the technology can be used in large-scale bioreactors to improve the understanding of the process itself and to test the validity of detailed computational models of the bioreactors in the future.

It is known that the processes of self-organization of the components of drying a liquid drop on a solid substrate are well reproduced under the same external conditions and are determined only by the composition and dispersion of the liquid. If the drop dries on the surface of the sensor device, these processes can be recorded and used as a passport characteristic of the liquid. The first half of the article is devoted to the description of the principles of the method and the proof of the validity of our assumptions. The second half of the article is devoted to the development of a user-friendly version of the device, where the change in the real and imaginary parts of the electrical impedance of the resonator was used as an informative parameter. The measure of the closeness of the relative positions of the hodographs of the compared samples on the complex plane is used as a criterion for the similarity-/-difference of various liquids. The design of a new sensor device and the results of its tests for distinguishing between different brands of alcoholic beverages and reconstituted milk of different concentrations are presented.

For the research and development of sensor systems, the collection and fusion of sensing data is the core. In order to make sensor data acquisition change with the change in environment, a dynamic data acquisition and fusion method based on feedback control is proposed in this paper. According to the sensing data acquisition and fusion model, the optimal acquisition of sensor data is achieved through real-time dynamic judgment of the collected data, decision-making of the next acquisition time interval, and adjustment. This model enables the sensor system to adapt to different environments. An experimental study of the proposed model was carried out on an experimental platform, and the results show that the proposed model can not only reflect the change in sensing data but also improve the transmission efficiency.

Gold nanoparticle (GNP)-labeled immunochromatography test strip (ICTS) has been widely used in different fields, but its sensitivity still require further improvement. In this work, a rapid and quantitative test strip detection method based on the photothermal effect of GNPs was established using a temperature sensor. A portable sensor device was fabricated based on the above method, and the main operating parameters were optimized. Three types of analyte models (cells, macromolecules, and small molecules) were chosen to evaluate the application of the sensor device using the commercial ICTS. The detection limit is at least 1 order of magnitude lower than that of the traditional visual detection ICTS. The other strip type of dot immunogold filtration test was adopted to further improve the sensitivity. The sensitivity of the sensor detection method was similar to that of the infrared camera method, and the proposed sensor device has obvious advantage of low-cost, as well as the usage of a small portable instrument, ease of use and rapid test. The portable sensor device based on the photothermal effect of GNPs can be used as a new and promising device for simple, quantitative, rapid, and on-site screening of analyte by ICTS.