Detection of Dementia-Related Abnormal Behaviour Using Recursive Auto-Encoders.

Age-related health issues have been increasing with the rise of life expectancy all over the world. One of these problems is cognitive impairment, which causes elderly people to have problems performing their daily activities. Detection of cognitive impairment at an early stage would enable medical doctors to deepen diagnosis and follow-up on patient status. Recent studies show that daily activities can be used to assess the cognitive status of elderly people. Additionally, the intrinsic structure of activities and the relationships between their sub-activities are important clues for capturing the cognitive abilities of seniors. Existing methods perceive each activity as a stand-alone unit while ignoring their inner structural relationships. This study investigates such relationships by modelling activities hierarchically from their sub-activities, with the overall goal of detecting abnormal activities linked to cognitive impairment. For this purpose, recursive auto-encoders (RAE) and their linear vs. greedy and supervised vs. semi-supervised variants are adopted to model the activities. Then, abnormal activities are systematically detected using RAE's reconstruction error. Moreover, to apply RAEs for this problem, we introduce a new sensor representation called raw sensor measurement (RSM) that captures the intrinsic structure of activities, such as the frequency and the order of sensor activations. As real-world data are not accessible, we generated data by simulating abnormal behaviour, which reflects on cognitive impairment. Extensive experiments show that RAEs can be used as a decision-supporting tool, especially when the training set is not labelled to detect early indicators of dementia

Ethical Surveillance: Applying Deep Learning and Contextual Awareness for the Benefit of Persons Living with Dementia

A significant proportion of the population has become used to sharing private information on the internet with their friends. This information can leak throughout their social network and the extent that personal information propagates can depend on the privacy policy of large corporations. In an era of artificial intelligence, data mining, and cloud computing, is it necessary to share personal information with unidentified people? Our research shows that deep learning is possible using relatively low capacity computing. When applied, this demonstrates promising results in spatio-temporal positioning of subjects, in prediction of movement, and assessment of contextual risk. A private surveillance system is particularly suitable in the care of those who may be considered vulnerable

Long Short Term Memory Based Model for Abnormal Behavior Prediction in Elderly Persons

Smart home refers to the independency and comfort that are ensured by remote monitoring and assistive services. Assisting an elderly person requires identifying and accurately predicting his/her normal and abnormal behaviors. Abnormal behaviors observed during the completion of activities of daily living are a good indicator that the person is more likely to have health and behavioral problems that need intervention and assistance. In this paper, we propose a method, based on long short-term memory recurrent neural networks (LSTM), to automatically predicting an elderly person’s abnormal behaviors. Our method allows to model the temporal information expressed in the long sequences collected over time. Our study aims to evaluate the performance of LSTM on identifying and predicting elderly persons abnormal behaviors in smart homes. We experimentally demonstrated, through extensive experiments using a dataset, the suitability and performance of the proposed method in predicting abnormal behaviors with high accuracy. We also demonstrated the superiority of the proposed method compared to the existing state-of-the-art methods

Variation in the formation of the median nerve involving a communicating branch from the musculocutaneous nerve

Background: The brachial plexus has a complex anatomical structure from its origin in the neck, through its course in the axillary region. It is formed by the union of the ventral rami of the spinal nerves from C5 to T1. Anomalies in the formation of the cords of brachial plexus and the communications between its branches are common. Case Report: Anomalies of the brachial plexus in terms of the formation of the median nerve were observed during routine dissection of the right upper limb of the cadaver of a 55-year-old male. The branches of the lateral and medial cords of the brachial plexus were dissected carefully and the variation from the usual pattern was noted. The lateral root of the median nerve arose from the lateral cord and bifurcated after 2 cm. The upper part united with the medial root of the median nerve and the lower part united with the median nerve approximately 0.5 cm past the aforementioned unification. There was also a communicating branch originating from the musculocutaneous nerve that arose 6 cm distal to the origin of the lateral root. This communicating branch coursed medially and united with the median nerve. Conclusions: It is important to be aware of these variations when planning surgery and regional anesthesia of the axilla or upper extremity. In addition, the presence of anatomic variations of the peripheral nervous system is often used to explain unexpected clinical signs and symptoms

MRI determination of conus medullaris level in an adult population in Turkey

The level of the conus medullaris (CM) in adults has been described in many studies with cadavers and living people. T1-weighted sagittal spin-echo MR images of the lumbar spine were reviewed in 364 consecutive patients (207 women, 157 men) with a mean age of 45 years (range 18–80). The most common level of CM was the L1-L2 intervertebral disc level in females and the T12-L1 intervertebral disc level in males. The distribution of CM location in a large adult population was shown to range from the T11-T12 intervertebral disc level to L2-L3 intervertebral disc level

A Survey on Ambient Sensor-Based Abnormal Behaviour Detection for Elderly People in Healthcare

Peer reviewed: TrueWith advances in machine learning and ambient sensors as well as the emergence of ambient assisted living (AAL), modeling humans’ abnormal behaviour patterns has become an important assistive technology for the rising elderly population in recent decades. Abnormal behaviour observed from daily activities can be an indicator of the consequences of a disease that the resident might suffer from or of the occurrence of a hazardous incident. Therefore, tracking daily life activities and detecting abnormal behaviour are significant in managing health conditions in a smart environment. This paper provides a comprehensive and in-depth review, focusing on the techniques that profile activities of daily living (ADL) and detect abnormal behaviour for healthcare. In particular, we discuss the definitions and examples of abnormal behaviour/activity in the healthcare of elderly people. We also describe the public ground-truth datasets along with approaches applied to produce synthetic data when no real-world data are available. We identify and describe the key facets of abnormal behaviour detection in a smart environment, with a particular focus on the ambient sensor types, datasets, data representations, conventional and deep learning-based abnormal behaviour detection methods. Finally, the survey discusses the challenges and open questions, which would be beneficial for researchers in the field to address.</jats:p

MRI determination of conus medullaris level in an adult population in Turkey

The level of the conus medullaris (CM) in adults has been described in many studies with cadavers and living people. T1-weighted sagittal spin-echo MR images of the lumbar spine were reviewed in 364 consecutive patients (207 women, 157 men) with a mean age of 45 years (range 18-80). The most common level of CM was the L1-L2 intervertebral disc level in females and the T12-L1 intervertebral disc level in males. The distribution of CM location in a large adult population was shown to range from the T11-T12 intervertebral disc level to L2-L3 intervertebral disc level

Effects of Nigella sativa on apoptosis and GABA(A) receptor density in cerebral cortical and hippocampal neurons in pentylenetetrazol induced kindling in rats

We investigated the effects of Nigella sativa on apoptosis and gamma-aminobutyric acid (GABA(A)) receptor density in cerebral cortical and hippocampal neurons in a pentylenetetrazol (PTZ)-induced kindling model in rats. The PTZ kindling model was produced by injecting PTZ in subconvulsive doses to rats on days 1, 3, 5, 8, 10, 12, 15, 17, 19, 22 and 24 of the study into animals of PTZ treated (PTZ) and PTZ + N. sativa treated (PTZ + NS) groups. Clonic and tonic seizures were induced by injecting a convulsive dose of PTZ on day 26 of the study. Rats in the PTZ + NS group were treated also with a 10 mg/kg methanolic extract of N. sativa 2 h before each PTZ injection. Rats in the control group were treated with 4 ml/kg saline. The number of neurons that expressed GABA(A) receptors in the hippocampus and cerebral cortex of rats in the PTZ and PTZ + NS groups increased significantly. There was no significant difference in the number of GABA(A) receptors between the PTZ and PTZ + NS groups. GABA(A) receptor density of the neurons in the cerebral cortex, but not hippocampus, was increased in PTZ group compared to controls. We observed a significant increase in the number of apoptotic neurons in the cerebral cortex of rats of both the PTZ and PTZ + NS groups compared to controls. We observed a significant decrease in the number of the apoptotic neurons in the cerebral cortex of rats in the PTZ + NS group compared to the PTZ group. N. sativa treatment ameliorated the PTZ induced neurodegeneration in the cerebral cortex as reflected by neuronal apoptosis and neuronal GABA(A) receptor frequency