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This paper presents a fully-printed chipless RFID sensor tag for short-range item identification and humidity monitoring applications. The tag consists of two planar inductor-capacitor resonators operating wirelessly through inductive coupling. One resonator is used to encode ID data based on frequency spectrum signature, and another one works as a humidity sensor, utilizing a paper substrate as a sensing material. The sensing performances of three paper substrates including commercial packaging paper are investigated. The use of paper provides excellent sensitivity and reasonable response time to humidity. The cheap and robust packaging paper, particularly, exhibits the largest sensitivity over the RH range from 20% to 70%, which offers the possibility of directly printing the sensor tag on traditional packages to make the package 'intelligent' at ultra-low cost.

Background: Compensatory movements are commonly employed by stroke survivors during seated reaching and may have negative effects on their long-term recovery. Detecting compensation is useful for coaching the patient to reduce compensatory trunk movements and improving the motor function of the paretic arm. Sensor-based and camera-based systems have been developed to detect compensatory movements, but they still have some limitations, such as causing object obstructions, requiring complex setups and raising privacy concerns. To overcome these drawbacks, this paper proposes a compensatory movement detection system based on pressure distribution data and is unobtrusive, simple and practical. Machine learning algorithms were applied to classify compensatory movements automatically. Therefore, the purpose of this study was to develop and test a pressure distribution-based system for the automatic detection of compensation movements of stroke survivors using machine learning algorithms. Methods: Eight stroke survivors performed three types of reaching tasks (back-and-forth, side-to-side, and up-anddown reaching tasks) with both the healthy side and the affected side. The pressure distribution data were recorded, and five features were extracted for classification. The k-nearest neighbor (k-NN) and support vector machine (SVM) algorithms were applied to detect and categorize the compensatory movements. The surface electromyography (sEMG) signals of nine trunk muscles were acquired to provide a detailed description and explanation of compensatory movements. Results: Cross-validation yielded high classification accuracies (F1-score>0.95) for both the k-NN and SVM classifiers in detecting compensation movements during all the reaching tasks. In detail, an excellent performance was achieved in discriminating between compensation and noncompensation (NC) movements, with an average F1score of 0.993. For the multiclass classification of compensatory movement patterns, an average F1-score of 0.981 was achieved in recognizing the NC, trunk lean-forward (TLF), trunk rotation (TR) and shoulder elevation (SE) movements. Conclusions: Good classification performance in detecting and categorizing compensatory movements validated the feasibility of the proposed pressure distribution-based system. Reliable classification accuracy achieved by the machine learning algorithms indicated the potential to monitor compensation movements automatically by using the pressure distribution-based system when stroke survivors perform seated reaching tasks.

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In this paper, we propose a piezoelectric energy harvester to scavenge the impact energy from human footsteps at low input frequencies. The device consists of an amplification mechanism and piezoelectric bimorphs. When a human foot strikes the proposed harvester, the amplification mechanism deforms the piezoelectric bimorphs in the 31-mode to produce a large mechanical strain, meaning that the output power can be generated with high efficiency. A maximum output power of 27.5 mW was generated by the proposed harvester at an input frequency of 1.5 Hz (representing fast walking), while 18.6 mW was generated at 1.0 Hz (representing slow walking). Comparison experiments also showed that the proposed harvester can produce much a higher output power than that the same harvester operating in the 33-mode under the same working conditions.