Human fall due to an accident can cause heavy injuries which may lead to a major medical issue for elderly people. With the introduction of new advanced technologies in the healthcare sector, an alarm system can be developed to detect a human fall. This paper summarizes various human fall detection methods and techniques, through observing people’s daily routine activities. A human fall detection system can be designed using one of these technologies: wearable based device, context-aware based and vision based methods. In this paper, we discuss different machine learning models designed to detect human fall using these techniques. These models have already been designed to discriminate fall from activities of daily living (ADL) like walking, moving, sitting, standing, lying and bending. This paper is aimed at analyzing the effectiveness of these machine learning algorithms for the detection of human fall.

BackgroundCerebral cavernous malformations (CCMs) frequently manifest with haemorrhages. Stereotactic radiosurgery (SRS) has been employed for CCM not suitable for resection. Its effect on reducing haemorrhage risk is still controversial. The aim of this study was to expand on the safety and efficacy of SRS for haemorrhagic CCM.MethodsThis retrospective multicentric study included CCM with at least one haemorrhage treated with single-session SRS. The annual haemorrhagic rate (AHR) was calculated before and after SRS. Recurrent event analysis and Cox regression were used to evaluate factors associated with haemorrhage. Adverse radiation effects (AREs) and occurrence of new neurological deficits were recorded.ResultsThe study included 381 patients (median age: 37.5 years (Q1–Q3: 25.8–51.9) with 414 CCMs. The AHR from diagnosis to SRS excluding the first haemorrhage was 11.08 per 100 CCM-years and was reduced to 2.7 per 100 CCM-years after treatment. In recurrent event analysis, SRS, HR 0.27 (95% CI 0.17 to 0.44), p<0.0001 was associated with a decreased risk of haemorrhage, and the presence of developmental venous anomaly (DVA) with an increased risk, HR 1.60 (95% CI 1.07 to 2.40), p=0.022. The cumulative risk of first haemorrhage after SRS was 9.4% (95% CI 6% to 12.6%) at 5 years and 15.6% (95% CI% 9 to 21.8%) at 10 years. Margin doses> 13 Gy, HR 2.27 (95% CI 1.20 to 4.32), p=0.012 and the presence of DVA, HR 2.08 (95% CI 1.00 to 4.31), p=0.049 were factors associated with higher probability of post-SRS haemorrhage. Post-SRS haemorrhage was symptomatic in 22 out of 381 (5.8%) patients, presenting with transient (15/381) or permanent (7/381) neurological deficit. ARE occurred in 11.1% (46/414) CCM and was responsible for transient neurological deficit in 3.9% (15/381) of the patients and permanent deficit in 1.1% (4/381) of the patients. Margin doses >13 Gy and CCM volume >0.7 cc were associated with increased risk of ARE.ConclusionSingle-session SRS for haemorrhagic CCM is associated with a decrease in haemorrhage rate. Margin doses ≤13 Gy seem advisable.

Learning has transcended into a life-long endeavor in the information age. It is no longer restricted to confines of formal classrooms. Consequently, a student is not restricted to traditional learning resources like teachers, textbooks or printed content. Digital resources available on the Internet form a very significant component of self-learning. Copious volumes of learning resources without legal barriers to self-learning reside in digital repositories, educational institution portals and on numerous websites. Learners wishing to utilize the web for personalized learning are faced with a daunting array of content to wade through and select the suitable ones to fulfill his/her learning objectives. Therefore, it is not a question of availability; it is one of relevance and suitability. Typically, in addition to time constraints, learners lack the expertise to screen content for effective eLearning. Adaptive hypermedia systems (AHSs) offer a path to harnessing this large volume of learning resources for personalized learning. This review paper provides a concise and coherent discussion about the evolution of AHSs along with the challenges that need to be addressed for effectively harnessing openly available educational resources referred to as open corpus resources (OCRs).

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