Flexible piezoelectric acoustic sensors have been developed to generate multiple sound signals with high sensitivity, shifting the paradigm of future voice technologies. Speech recognition based on advanced acoustic sensors and optimized machine learning software will play an innovative interface for artificial intelligence (AI) services. Collaboration and novel approaches between both smart sensors and speech algorithms should be attempted to realize a hyperconnected society, which can offer personalized services such as biometric authentication, AI secretaries, and home appliances. Here, representative developments in speech recognition are reviewed in terms of flexible piezoelectric materials, self‐powered sensors, machine learning algorithms, and speaker recognition.

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Inorganic-based micro light-emitting diodes (µLEDs) have witnessed significant improvements in terms of display and biomedical applications, which can shift the paradigm of future optoelectronic systems. In particular, µLED displays are on the verge of becoming the next big interface platform for visual communications, expanding to various internet of things and wearable/bioapplications. Novel µLED concepts need to be upgraded to be able to satisfy their potential optoelectric applications, such as virtual reality, smart watches, and medical sensors for individual computing in this hyperconnected society. Here, representative progresses in the field of flexible µLEDs are reviewed with regard to device structures, massive µLED transfers, methods for performance enhancement, and applications.

Little was known about work situation and work-related difficulties, including housework after stomach cancer diagnosis. We aimed to compare employment status and work-related difficulties between stomach cancer survivors and the general population. We enrolled 408 stomach cancer survivors from two hospitals 28 months after diagnosis and 994 representative volunteers from the general population from 15 geographic districts. Working was defined as being employed (including self-employed) and nonworking as being retired or a homemaker. Nonworking was significantly higher among stomach cancer survivors (46.6%) than in the general population (36.5%). Compared with the general population, the survivors had more fatigue in performing both housework (adjusted odds ratio (aOR) ¼ 2.08; 95% confidence interval (95% CI) ¼ 1.01 -4.29) and gainful work (aOR ¼ 4.02; 2.55 -6.33). More cancer survivors had reduced working hours (aOR ¼ 1.42; 95% CI ¼ 4.60 -28.35) and reduced work-related ability (aOR ¼ 6.11; 95% CI ¼ 3.64 -10.27) than did the general population. The association of nonworking with older age and being female was significantly more positive for survivors than for the general population. Among survivors, poorer Eastern Cooperation Oncology Group Performance Status and receiving total gastrectomy were positively associated with nonworking. Stomach cancer survivors experienced more difficulties in both housework and gainful employment than did the general population. Our findings on stomach cancer survivors' work-related difficulties and the predictors of nonworking will help physicians guide patients towards more realistic postsurgical employment plans.

Flexible resonant acoustic sensors have attracted substantial attention as an essential component for intuitive human-machine interaction (HMI) in the future voice user interface (VUI). Several researches have been reported by mimicking the basilar membrane but still have dimensional drawback due to limitation of controlling a multifrequency band and broadening resonant spectrum for full-cover phonetic frequencies. Here, highly sensitive piezoelectric mobile acoustic sensor (PMAS) is demonstrated by exploiting an ultrathin membrane for biomimetic frequency band control. Simulation results prove that resonant bandwidth of a piezoelectric film can be broadened by adopting a lead-zirconate-titanate (PZT) membrane on the ultrathin polymer to cover the entire voice spectrum. Machine learning–based biometric authentication is demonstrated by the integrated acoustic sensor module with an algorithm processor and customized Android app. Last, exceptional error rate reduction in speaker identification is achieved by a PMAS module with a small amount of training data, compared to a conventional microelectromechanical system microphone.

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