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The average attention span of humans is decreasing. To adapt to the change, researchers investigate visual and auditory techniques that alter the perception of time passing. However, the auditory technique is underexplored in comparison to visual techniques, which are becoming increasingly important as voice assistants, speech interfaces or time-aware computational/robotic systems proliferate. This paper investigated the effect of pitch and interval variables on time perception. Three pitch levels (static, falling, rising) and two interval patterns (static, narrowing) for a total of six auditory stimuli were compared. Principal findings indicated that rising/falling pitch and interval narrowing, as well as their combinations, can be used to shorten the perception of time. This research contributes to a better understanding of end-users' subjective temporal estimation of auditory stimuli, which can be used to further develop audio-based interactive devices/agents. [ABSTRACT FROM AUTHOR]

Gait analysis is a powerful technique that detects and identifies foot disorders and walking irregularities, including pronation, supination, and unstable foot movements. Early detection can help prevent injuries, correct walking posture, and avoid the need for surgery or cortisone injections. Traditional gait analysis methods are expensive and only available in laboratory settings, but new wearable technologies such as AI and IoT-based devices, smart shoes, and insoles have the potential to make gait analysis more accessible, especially for people who cannot easily access specialized facilities. This research proposes a novel approach using IoT, edge computing, and tiny machine learning (TinyML) to predict gait patterns using a microcontroller-based device worn on a shoe. The device uses an inertial measurement unit (IMU) sensor and a TinyML model on an advanced RISC machines (ARM) chip to classify and predict abnormal gait patterns, providing a more accessible, cost-effective, and portable way to conduct gait analysis. [ABSTRACT FROM AUTHOR]

Mindfulness practices are well-known for their benefits to mental and physical well-being. Given the prevalence of smartphones, mindfulness applications have attracted growing global interest. However, the majority of existing applications use guided meditation that is not adaptable to each user’s unique needs or pace. This article proposes a novel framework called Attention Regulation Framework (ARF), which studies how more flexible and adaptable mindfulness applications could be designed, beyond guided meditation and toward self-regulated meditation. ARF proposes mindfulness interaction design guidelines and interfaces whereby practitioners naturally and constantly bring their attention back to the present moment and develop nonjudgmental awareness. This is achieved by the performance of subtle movements, which are supported by non-intrusive detection-feedback mechanisms. We used two design cases to demonstrate ARF in static and kinetic meditation conditions. We conducted four user evaluation studies in unique situations where ARF is particularly effective, vis-à-vis mindfulness practice in busy environments and mindfulness interfaces that adapt to the pace of the user. The studies show that the design cases, compared with guided meditation applications, are more effective in improving attention, mindfulness, mood, well-being, and physical balance. Our work contributes to the development of self-regulated mindfulness technologies. [ABSTRACT FROM AUTHOR]