Mobile applications on smartphones and tablets have become part of our everyday lives. The number of augmented reality (AR) technology applications is also increasing. Augmented reality has proven to be effective in various areas of human life, from education, marketing, and training to navigation. All people have the right to access information and use available technologies, but not everyone has the same opportunities. To contribute to the digital inclusion of people who are often disadvantaged in this regard, we should think about the accessibility of digital technologies, including mobile augmented reality (MAR). The specificity of MAR is a new way of human–computer interaction compared to traditional mobile solutions. The objective of this review paper is to analyze the handheld AR solutions developed for people with different disabilities to identify accessibility challenges related to interaction when performing different tasks in AR. It also explores and presents accessibility features and other accessibility best practices, as well as potential future research directions related to the personalization and customization of such solutions for individuals. The results of this literature review can contribute to the creation of accessibility guidelines in the field of handheld AR and encourage the development of accessible AR solutions that can benefit not only people with disabilities but also people without disabilities.

In recent years there has been an increasing need for miniature, low-cost, commercially accessible, and user-friendly sensor solutions for wireless body area networks (WBAN), which has led to the adoption of new physical communication interfaces providing distinctive advantages over traditional wireless technologies. Ultra-wideband (UWB) and intrabody communication (IBC) have been the subject of intensive research in recent years due to their promising characteristics as means for short-range, low-power, and low-data-rate wireless interfaces for interconnection of various sensors and devices placed on, inside, or in the close vicinity of the human body. The need for safe and standardized solutions has resulted in the development of two relevant standards, IEEE 802.15.4 (for UWB) and IEEE 802.15.6 (for UWB and IBC), respectively. This paper presents an in-depth overview of recent studies and advances in the field of application of UWB and IBC technologies for wireless body sensor communication systems.

Musculoskeletal models play an essential role in ankle rehabilitation research. The majority of the existing models have established the relationship between EMG and joint torque. However, EMG signal acquisition requires higher clinical conditions, such as sensitivity to external circumstances, such as motion artifacts and electrode position. To solve the nonlinear and time-varying nature of joint movement, a Functional Electrical Stimulation (FES) model was proposed in this study to simulate the whole process of ankle dorsiflexion. The model is combined with muscle contraction dynamics based on Hill model and ankle inverse dynamics to connect FES parameters, torques, and ankle angles. In addition, the extended Kalman filter (EKF) algorithm was applied to identify the unknown parameters of the model. Model validation experiment was performed by acquiring the actual data of healthy volunteers. Results showed that the root mean square error (RMSE) and normalized root mean square error (NRMSE) of this model were 11.93%0.53% and 1.390.26, respectivelywhich means it can effectively predict the output variation of ankle joint angle while changing electrical stimulation parameters. Therefore, the proposed mode is essential for developing closed-loop feedback control of electrical stimulation and has the potential to help patients to conduct rehabilitation training.

Objectives : During Neuromuscular Electrical Stimulation (NMES), a real-time monitoring method is urgently needed to reduce the negative effect induced by muscle fatigue. Technology or Method : A novel method named electrical impedance myography (EIM) is proposed to evaluate muscle fatigue degree during NMES. The experiments were performed on the anterior tibialis muscle on several voluunteers. Two EIM parameters: impedance amplitude (|Z|) and phase (θ), were measured in real time, while applying six NMES parameter combinations. The mean power frequency (MPF) of the surface electromyography (sEMG) was selected for verification of the proposed method. Results : |Z| and θ of EIM signals decreased significantly after NMES (p < 0.05). They also both showed a linear downward trend during NMES, which was also observed in MPF of sEMG signals. At the measurement frequency of 20 kHz, steady-state fatigue was achieved when |Z| decreased to approximately 14% of the initial value. Clinical or Biological Impact : This study provides a method for monitoring muscle fatigue induced by NMES, which is beneficial to the application of NMES.

The concept of weakly hard real-time systems can be used to model real-time systems that may tolerate occasional deadline misses in a bounded and predictable manner. This model applies to many practical applications and is particularly interesting in the context of real-time control systems. In practice, applying hard real-time constraints may be too rigid since a certain amount of deadline misses is acceptable in some applications. In order to maintain system stability, limitations on the amount and distribution of violated deadlines need to be imposed. These limitations can be formally expressed as weakly hard real-time constraints. Current research in the field of weakly hard real-time task scheduling is focused on designing scheduling algorithms that guarantee the fulfillment of constraints, while aiming to maximize the total number of timely completed task instances. This paper provides an extensive literature review of the work related to the weakly hard real-time system model and its link to the field of control systems design. The weakly hard real-time system model and the corresponding scheduling problem are described. Furthermore, an overview of system models derived from the generalized weakly hard real-time system model is provided, with an emphasis on models that apply to real-time control systems. The state-of-the-art algorithms for scheduling tasks with weakly hard real-time constraints are described and compared. Finally, an overview of controller design methods that rely on the weakly hard real-time model is given.

Obstructive sleep apnea (OSA) is a chronic breathing disorder during sleep that affects 10-30% of adults in North America. The gold standard for diagnosing OSA is polysomnography (PSG). However, PSG has several drawbacks, for example, it is a cumbersome and expensive procedure, which can be quite inconvenient for patients. Additionally, patients often have to endure long waitlists before they can undergo PSG. As a result, other alternatives for screening OSA have gained attention. Speech, as an accessible modality, is generated by variations in the pharyngeal airway, vocal tract, and soft tissues in the pharynx, which shares similar anatomical structures that contribute to OSA. Consequently, in this study, we aim to provide a comprehensive review of the existing research on the use of speech for estimating the severity of OSA. In this regard, a total of 851 papers were initially identified from the PubMed database using a specified set of keywords defined by population, intervention, comparison and outcome (PICO) criteria, along with a concatenated graph of the 5 most cited papers in the field extracted from ConnectedPapers platform. Following a rigorous filtering process that considered the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach, 32 papers were ultimately included in this review. Among these, 28 papers primarily focused on developing methodology, while the remaining 4 papers delved into the clinical perspective of the association between OSA and speech. In the next step, we investigate the physiological similarities between OSA and speech. Subsequently, we highlight the features extracted from speech, the employed feature selection techniques, and the details of the developed models to predict OSA severity. By thoroughly discussing the current findings and limitations of studies in the field, we provide valuable insights into the gaps that need to be addressed in future research directions.