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Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.

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Hydatid disease is one of the most complicated and devastating conditions caused by Echinococcus granulosus . This globally distributed disease continues to be an important public health concern in many low- and middle-income countries. The liver and the lungs are the most frequently involved sites, but virtually any organ system can be affected. Osseous hydatidosis is relatively less common, but it is extremely debilitating and difficult to manage due to frequent recurrences. Patients often demonstrate a delayed clinical presentation as bone involvement is predominantly a silent and slowly progressive disease with a long latent period. Radiological investigations play an important role in the diagnosis. Although standard therapeutic guidelines are not available, the treatment of choice is a combination of chemotherapy and surgery. Clinicians should perform a lifelong follow-up for early detection of potential recurrence and sequels. This paper aims to highlight hydatid disease of the pelvic bone as an important differential diagnosis of tubercular hip arthritis, especially in areas with high echinococcosis prevalence.

The Internet of Things (IoT) is an emerging field consisting of Internet-based globally connected network architecture. A subset of IoT is the Internet of Healthcare Things (IoHT) that consists of smart healthcare devices having significant importance in monitoring, processing, storing, and transmitting sensitive information. It is experiencing novel challenges regarding data privacy protection. This article discusses different components of IoHT and categorizes various healthcare devices based on their functionality and deployment. This article highlights the possible points and reasons for data leakage, such as conflicts in laws, the use of sub-standard devices, lack of awareness, and the non-availability of dedicated local law enforcement agencies. This article draws attention to the escalating demand for a suitable regulatory framework and analyzes compliance problems of IoHT devices concerning healthcare data privacy and protection regulations. Furthermore, the article provides some recommendations to improve the security and privacy of IoHT implementation.

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The fabrication of lightweight, ultra-thin, low power and intelligent body-borne sensors leads to novel advances in wireless body area networks (WBANs). Depending on the placement of the nodes, it is characterized as in/on body WBAN; thus, the channel is largely affected by body posture, clothing, muscle movement, body temperature and climatic conditions. The energy resources are limited and it is not feasible to replace the sensor’s battery frequently. In order to keep the sensor in working condition, the channel resources should be reserved. The lifetime of the sensor is very crucial and it highly depends on transmission among sensor nodes and energy consumption. The reliability and energy efficiency in WBAN applications play a vital role. In this paper, the analytical expressions for energy efficiency (EE) and packet error rate (PER) are formulated for two-way relay cooperative communication. The results depict better reliability and efficiency compared to direct and one-way relay communication. The effective performance range of direct vs. cooperative communication is separated by a threshold distance. Based on EE calculations, an optimal packet size is observed that provides maximum efficiency over a certain link length. A smart and energy efficient system is articulated that utilizes all three communication modes, namely direct, one-way relay and two-way relay, as the direct link performs better for a certain range, but the cooperative communication gives better results for increased distance in terms of EE. The efficacy of the proposed hybrid scheme is also demonstrated over a practical quasi-static channel. Furthermore, link length extension and diversity is achieved by joint network-channel (JNC) coding the cooperative link.

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