Numerous studies have extensively examined the living conditions of older adults who live alone, highlighting falls as the most significant threat to their well-being. Recognizing the critical importance of fall detection for older adults, researchers have conducted numerous investigations over the past decade to develop fall detection systems utilizing various detection methods. In this article, we further explore the potential of acoustic signals in detecting fall events and propose a novel system called FA-Fall, which fully harnesses acoustic signals for fall detection. Our approach revolves around fusing passive and active acoustic sensing through a pair of audio transceivers. We design a multimodal classification framework that integrates an attention mechanism and an anomaly detection mechanism to capitalize on the complementary and redundancy of passive and active sensing. We implemented the FA-Fall system and carried out extensive experiments to evaluate its performance. The results demonstrate that FA-Fall achieves an impressive overall accuracy of 98.97% under typical environmental conditions. Furthermore, it can still detect fall events with over 90% accuracy in challenging environments characterized by considerable background noise or nonline-of-sight conditions.

This paper presents a breakthrough framework for smartphone user authentication by analyzing the physiology and behavior of articulation, namely the vocal tract, tongue position, and lip movement, to expose individual uniqueness during uttering. The main idea is to leverage a smartphone's speaker and microphone to transmit and receive speech and ultrasonic signals, construct identity-related features, and determine whether the samples are legitimate users or attackers. The proposed system requires a smaller number of samples by utilizing single utterances resulting that the system resisting playback and mimicry attacks with an average accuracy of 99% in three different scenarios.

Current voice-based user authentication explores the unique characteristics from either the voiceprint or mouth movements, which are at risk to replay attacks. During speaking, the vocal tract, tongue, and lip, including the static shape and dynamic movements, expose individual uniqueness, and adversaries hardly imitate them. Moreover, most voice-based user authentications are passphrase-dependent, which significantly reduces the user experience. Therefore, our work aims to employ the individual uniqueness of vocal tract, tongue, lip movement to realize user authentication on a smartphone. This paper presents a new authentication framework to identify smartphone users through articulation, namely tongue and lip motion reading. The main idea is to capture acoustic and ultrasonic signals from a mobile phone and analyze the fine-grained impact of articulation movement on the uttered words. We currently develop a passphrase-independent authentication model by analyzing the articulation in continuous speech, exploring different scenarios, and creating a passphrase-independent authentication model.

The vowel is deemed the essence of the syllable in which controls the articulation of each word uttered. However, articulation sensing has not been adequately evaluated. The challenging task is that the speech signal contains insufficient information for articulation analysis. We propose a new approach to identify the articulation of monophthongs in multiple languages. We employ simultaneously two ranges of acoustic signals, both speech and ultrasonic signal, to recognize lip shape and tongue position, which is implemented into an off-the-shelf smartphone to be more accessible. The articulation recognition accuracy is 94.74%. The proposed system also applies to an alternative model for a pronunciation training system that gives articulation feedback to a user.

Strength training is essential for both physical and mental well-being. Muscular mass and strength gain can help with weight loss, balance improvement, and fall prevention. The neuromuscular connection, or mind-muscle connection, is critical for improving strength training performance. However, many fitness trackers and applications are missing a feature that allows users to track their neuromuscular workout performance. The goal is to immerse the user experience while keeping the cost and size of the healthcare device to a minimum. A wearable EEG hairband and EMG shirt are outfitted with dry and non-invasive bio-signal detecting that securely attaches to the body's surface during exercise. Participants in our study are exposed to five upper-limb free-weight exercises. The result shows that low-intensity exercise can increase upper-limp muscle contraction by over 30%, and individuals with mental effort have an average precision of 81%.