Background: Sentiment analysis is a significant yet difficult task in natural language processing. The linguistic peculiarities of Cantonese, including its high similarity with Standard Chinese, its grammatical and lexical uniqueness, and its colloquialism and multilingualism, make it different from other languages and pose additional challenges to sentiment analysis. Recent advances in models such as ChatGPT offer potential viable solutions. Objective: This study investigated the efficacy of GPT-3.5 and GPT-4 in Cantonese sentiment analysis in the context of web-based counseling and compared their performance with other mainstream methods, including lexicon-based methods and machine learning approaches. Methods: We analyzed transcripts from a web-based, text-based counseling service in Hong Kong, including a total of 131 individual counseling sessions and 6169 messages between counselors and help-seekers. First, a codebook was developed for human annotation. A simple prompt (“Is the sentiment of this Cantonese text positive, neutral, or negative? Respond with the sentiment label only.”) was then given to GPT-3.5 and GPT-4 to label each message’s sentiment. GPT-3.5 and GPT-4’s performance was compared with a lexicon-based method and 3 state-of-the-art models, including linear regression, support vector machines, and long short-term memory neural networks. Results: Our findings revealed ChatGPT’s remarkable accuracy in sentiment classification, with GPT-3.5 and GPT-4, respectively, achieving 92.1% (5682/6169) and 95.3% (5880/6169) accuracy in identifying positive, neutral, and negative sentiment, thereby outperforming the traditional lexicon-based method, which had an accuracy of 37.2% (2295/6169), and the 3 machine learning models, which had accuracies ranging from 66% (4072/6169) to 70.9% (4374/6169). Conclusions: Among many text analysis techniques, ChatGPT demonstrates superior accuracy and emerges as a promising tool for Cantonese sentiment analysis. This study also highlights ChatGPT’s applicability in real-world scenarios, such as monitoring the quality of text-based counseling services and detecting message-level sentiments in vivo. The insights derived from this study pave the way for further exploration into the capabilities of ChatGPT in the context of underresourced languages and specialized domains like psychotherapy and natural language processing.

Post-stroke patients usually suffer from a higher fall risk. Identifying potential fallers and giving them proper attention could reduce their chance of a fall that results in severe injuries and decreased quality of life. In this study, we introduced a novel approach for fall risk prediction that evaluates Short-form Berg Balance Scale scores via inertial measurement unit data measured from a 3-meter timed-up-and-go test. This approach used sensor technology and was thus easy to implement, and allowed a quantitative analysis of both gait and balance. The results showed that elastic net logistic regression achieved the best performance with 85% accuracy and 88% area under the curve compared with support vector machine, least absolute shrinkage and selection operator (LASSO), and stepwise logistic regression. This paper provides a framework for using sensor-based features together with a feature-selection strategy for screening and predicting the fall risk of post-stroke patients in a convenient setup with high accuracy. The findings of this study will not only enable the assessment of fall risk among post-stroke patients in a cost-effective manner but also provide decision-making support for community care providers and medical professionals in the form of sensor-based data on gait performance.

Background: Sleep is related to various kinds of health outcomes. Many studies traditionally collect data on sleep using questionnaires or sleep diaries. An increasing popular alternative is a wrist-worn device. The accuracy of these devices is uncertain, and assessment of this accuracy is important. Introduction: The purpose of this study is to compare consensus sleep diary (CSD) and an actigraphy-based wrist-worn device (Fitbit Alta™ [Fitbit, San Francisco, CA]) measurements of total sleep time (TST), sleep onset latency, wake time after sleep onset, number of awakenings, and sleep efficiency. Materials and Methods: Ten healthy young adults (50% female, 100% Asian) in the age range between 20 to 24 years old wore a Fitbit Alta around their nondominated hand during seven consecutive nights. The participants also filled in a CSD every day. Results: On average, the wrist-worn device (Fitbit Alta) recorded a TST per night of 437.15 min, which is 5.46 min shorter on average than the CSD recorded (442.61 min). Bland–Altman plots indicate that there is large variance in the sleep recorded between Fitbit™ (Fitbit, San Francisco, CA) and CSD. For example, Fitbit recorded 2.15 more awakenings per night than CSD, which is equal to 13.09 min on average longer wake time after sleep onset. Conclusion: Fitbit and CSD show significant differences in recording sleep. We find that for most sleep metrics, the level of disagreement is small enough for the devices to be interchangeably used except for recording wakes during the night.

Background: Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest. Objective: The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults. Methods: In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals. Results: The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN. Conclusions: The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community’s burden of continuous health monitoring.

This paper presents an AC-DC rectifier that can provide high output voltage and power conversion efficiency (PCE), especially in low RF input power. In order to maximize the output voltage and PCE, the key design parameters optimization of the charge pump is discussed. Besides, to reduce the threshold voltage, the conventional Schottky-diodes are replaced by diode-connected MOSFETs which utilize native transistors with ultra low threshold voltage. A 5-stage rectifier is simulated by using harmonic balance nonlinear circuit analysis and implemented in 0.18 μm 1P6M CMOS technology with 0.06 sq mm die size. Measurement results showed that if a 150 kΩ load resistance is parallel with the rectifier output, DC voltage can reach to 2.6 V and PCE reaches as high as 46.43 % with input power of -10 dBm at a frequency of 900 MHz so as to light up a 2.4 V light-emitting diode (LED) lamp.