Cervical cancer is the fourth most prevalent disease in women. Accurate and timely cancer detection can save lives. Automatic and reliable cervical cancer detection methods can be devised through the accurate segmentation and classification of Pap smear cell images. This paper presents an approach to whole cervical cell segmentation using a mask regional convolutional neural network (Mask R-CNN) and classifies this using a smaller Visual Geometry Group-like Network (VGG-like Net). ResNet10 is used to make full use of spatial information and prior knowledge as the backbone of the Mask R-CNN. We evaluate our proposed method on the Herlev Pap Smear dataset. In the segmentation phase, when Mask R-CNN is applied on the whole cell, it outperforms the previous segmentation method in precision (0.92±0.06), recall (0.91±0.05) and ZSI (0.91±0.04). In the classification phase, VGG-like Net is applied on the whole segmented cell and yields a sensitivity score of more than 96% with low standard deviation (±2.8%) for the binary classification problem and yields a higher result of more than 95% with low standard deviation (maximum 4.2% in accuracy measurement) for the 7-class problem in terms of sensitivity, specificity, accuracy, h-mean, and F1 score.
Most of people likes living independently at home. Some activity in our daily life is prone to have some accidents, such as falls. Falls can make people in fatal conditions, even death. A prototype of fall detection system using accelerometer and gyroscope based on smartphone is presented in this paper.
Accelerometerand gyroscope sensors are embedded in smartphone to get the result of fall detection more accurately.Automatic call as an alert will be sent to family members if someone using this application in fatal condition and need some help. This research also can distinguish condition of people between falls and activity daily living. Several scenarios were used in these experiments. The result showed that the proposed system could successfully record level of accuracy of the fall detection system till 93.3% in activity daily living and error detected of fall was 2%.
Elderly people with long-term care and some disabilities are more susceptible to falls. Fall can cause accidental or unintentional injury, even deaths. Fall detection monitoring is needed amongst elderly, particularly for elderly people who like living independently. Physical limitations and disabilities of elderly in doing their daily activities, which susceptible to fall, become the reason why fall detection is required. This paper presents a prototype of ubiquitous fall detection and alert system in smart home environment (u-FASt) using smartphone. This proposed system of u-FASt has three features, namely: (1) Sensing data gathered from accelerometer and gyroscope that embedded in the smartphone; (2) Real time alerting system consists of mobile alarm, SMS notification, and automatic calling; (3) History of falls that consists of time of fall, body position of fall, and location of fall. The result of the experiments showed that we could get 95.33% for accuracy level.
Older people with chronic conditions even lead to some disabilities face many challenges in performing daily life. Assistive robot is considered as a tool to provide companionship and assist daily life of older people and disabled people. This paper presents a review of assistive robotic technology, particularly for older people and disabled people. The result of this review constitutes a step towards the development of assistive robots capable of helping some problems of older people and disabled people. Hence, they may remain in at home and live independently.
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