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Position-aware indoor human activity recognition and fall detection
Full text linkWith increasing life expectancy, particularly in developed nations, the proportion of elderly individuals is rising rapidly, necessitating advanced systems for continuous monitoring and timely intervention to support independent living and enhance safety in assisted care environments. Falls are among the leading causes of hospitalisations and deaths related to injuries in this demographic, highlighting the urgent need for intelligent fall detection systems. However, most existing solutions struggle with real-world deployment due to incomplete anomaly modelling and a lack of contextual location awareness. This paper introduces a novel position-aware indoor activity recognition and fall detection approach that uses spatial and motion data to detect falls with high accuracy and contextual relevance. The system integrates Ultra-Wideband (UWB) positioning technology with a Multilayer Perceptron (MLP) model to achieve indoor localisation. Furthermore, accelerometer and gyroscope data are used for activity monitoring, which is processed using a hybrid deep learning architecture that combines a Variational Autoencoder (VAE), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. This architecture takes advantage of temporal and spatial feature extraction for improved fall detection. The localisation module achieves over 96% accuracy. For activity recognition, the VAE CNN-LSTM model achieving fall detection accuracy exceeding 97%. A late fusion decision layer combines spatial and activity-level insights to enable precise detection and localisation of fall events within indoor environments. The proposed system is validated in a real-world smart home setting and demonstrates strong performance in terms of accuracy, scalability, and adaptability
Term spread volatility as a leading indicator of economic activity
No full textIn this paper, we examine the macroeconomic predictive power of the volatility of the US Treasury yield curve slope (term spread volatility). Our forecasting exercise shows that US term spread volatility has significant predictive power for US industrial production and employment growth. The predictive power of term spread volatility is stronger at medium- and long-term forecasting horizons and remains robust when well-established predictors of economic activity, such as the term spread and stock market returns, are included. Our results also show that term spread volatility has statistically and economically distinct predictive power compared to other measures of economic uncertainty. Moreover, the predictive power of term spread volatility increases significantly after the 2008 Great Recession, indicating that the relationship between uncertainty about macroeconomic expectations and macroeconomic performance has strengthened in the post-Great Recession period. Finally, our out-of-sample forecasting results show that term spread volatility outperforms the term spread in forecasting economic activity over the longer term
Health justice partnerships: tackling inequality through integrated legal care
No full textCan embedding legal help inside health and community services measurably reduce inequality, improve wellbeing, and strengthen trust in justice systems?
This video sees Dr Curran discussing her research on tackling inequality through integrated legal care and what works and why and how
Development and psychometric evaluation of a Turkish adaptation of the Social Media Flow Scale
Full text linkThe present study adapted the Social Media Flow Scale (SMFS), developed by Brailovskaia et al. (2020), into Turkish and evaluated its psychometric properties. Data from 732 social media users (N = 732; 65.4% female; Mage = 31.19 years, SDage = 11.13) were collected by an online survey. A standard procedure, including forward and back translation, was used to ensure the linguistic validity of the Turkish SMFS. Confirmatory factor analysis supported the original five-factor structure, comprising focused attention, enjoyment, curiosity, telepresence, and time distortion. Fit indices revealed a good fit of the model (comparative fit index = .975, Tucker-Lewis index = .960, root mean square error of approximation = .066, and standardized root mean square residual = .033). All subscales demonstrated acceptable to excellent internal consistency (α = 0.789–0.888; ω = 0.791–0.942). Convergent and discriminant validity of the SMFS were supported by average variance extraction, composite reliability, and heterotrait-monotrait ratio of correlations. Analyses of concurrent validity showed that total scores on the SMFS were significantly positively related to social media continuance, social media-related fear of missing out, social media addiction, and problematic smartphone use (r = .515 to .689). The findings suggest that flow in social media use acts as a double-edged sword by both maintaining engagement and being associated with problematic use. In sum, the results indicate that the Turkish SMFS is a reliable and valid instrument for assessing multidimensional flow experiences in social media contexts and can be utilized in research on digital well-being and addictive behaviors
Off-label medications and non-invasive brain stimulation (NIBS) in the treatment of internet gaming disorder: a systematic review and meta-analysis
Full text linkInternet gaming disorder (IGD) has become a worldwide concern, but there are still no approved medications or treatments for IGD. The present study systematically meta-analyzed the available research regarding off-label medications and non-invasive brain stimulation (NIBS) as neurobiological treatments for IGD. A total of 18 studies met the inclusion criteria (n = 687; age range = 9 to 26 years) following PRISMA guidelines, and data from 15 studies were included in the meta-analysis. In some of the studies, IGD was a comorbid condition with major depression, alcohol use disorder, attention deficits, and/or hyperactivity disorder. The findings indicated that: (i) treatment using non-invasive neuromodulation (tDCS targeting dorsolateral prefrontal cortex [DLPFC]), bupropion, selective serotonin reuptake inhibitors (SSRIs), and methylphenidate were significant in reducing IGD; (ii) pharmacotherapy was much more effective than tDCS; (iii) there were no significant differences between tDCS, SSRIs, and methylphenidate, and all except SSRIs were outperformed by bupropion; (iv) the effect of tDCS, bupropion, and SSRIs were independent of IGD severity; (v) the effect of bupropion was independent of treatment duration, gaming level, or comorbidity with depression; (vi) the effect of bupropion remained stable after 12 weeks follow-up; (vii) the effect of methylphenidate was independent of treatment duration; and (viii) the effect of SSRIs was independent of IGD severity and treatment duration. However, this initial evidence is limited in its generalizability due to high risk of bias, overrepresentation of males, small sample sizes, lack of clinical interviews, lack of consideration of comorbidities, lack of monitoring for side effects, and insufficient details for exact replication
