In-house deep environmental sentience for smart homecare solutions toward ageing society.

With an increasing amount of elderly people needing home care around the clock, care workers are not able to keep up with the demand of providing maximum support to those who require it. As medical costs of home care increase the quality is care suffering as a result of staff shortages, a solution is desperately needed to make the valuable care time of these workers more efficient. This paper proposes a system that is able to make use of the deep learning resources currently available to produce a base system that could provide a solution to many of the problems that care homes and staff face today. Transfer learning was conducted on a deep convolutional neural network to recognize common household objects was proposed. This system showed promising results with an accuracy, sensitivity and specificity of 90.6%, 0.90977 and 0.99668 respectively. Real-time applications were also considered, with the system achieving a maximum speed of 19.6 FPS on an MSI GTX 1060 GPU with 4GB of VRAM allocated

Highly-efficient fog-based deep learning AAL fall detection system

[EN] Falls is one of most concerning accidents in aged population due to its high frequency and serious repercussion; thus, quick assistance is critical to avoid serious health consequences. There are several Ambient Assisted Living (AAL) solutions that rely on the technologies of the Internet of Things (IoT), Cloud Computing and Machine Learning (ML). Recently, Deep Learning (DL) have been included for its high potential to improve accuracy on fall detection. Also, the use of fog devices for the ML inference (detecting falls) spares cloud drawback of high network latency, non-appropriate for delay-sensitive applications such as fall detectors. Though, current fall detection systems lack DL inference on the fog, and there is no evidence of it in real environments, nor documentation regarding the complex challenge of the deployment. Since DL requires considerable resources and fog nodes are resource-limited, a very efficient deployment and resource usage is critical. We present an innovative highly-efficient intelligent system based on a fog-cloud computing architecture to timely detect falls using DL technics deployed on resource-constrained devices (fog nodes). We employ a wearable tri-axial accelerometer to collect patient monitoring data. In the fog, we propose a smart-IoT-Gateway architecture to support the remote deployment and management of DL models. We deploy two DL models (LSTM/GRU) employing virtualization to optimize resources and evaluate their performance and inference time. The results prove the effectiveness of our fall system, that provides a more timely and accurate response than traditional fall detector systems, higher efficiency, 98.75% accuracy, lower delay, and service improvement.This research was supported by the Ecuadorian Government through the Secretary of Higher Education, Science, Technology, and Innovation (SENESCYT) and has received funding from the European Union's Horizon 2020 research and innovation program as part of the ACTIVAGE project under Grant 732679.Sarabia-Jácome, D.; Usach, R.; Palau Salvador, CE.; Esteve Domingo, M. (2020). Highly-efficient fog-based deep learning AAL fall detection system. Internet of Things. 11:1-19. https://doi.org/10.1016/j.iot.2020.100185S11911“World Population Ageing.” [Online]. Available: http://www.un.org/esa/population/publications/worldageing19502050/. [Accessed: 23-Sep-2018].“Falls, ” World Health Organization. [Online]. Available: http://www.who.int/news-room/fact-sheets/detail/falls. 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IEEE Access, 6, 43563-43574. doi:10.1109/access.2018.2861331C. Wang et al., “Low-power fall detector using triaxial accelerometry and barometric pressure sensing,” vol. 12, no. 6, pp. 2302–2311, 2016.S.B. Khojasteh and E. De Cal, “Improving fall detection using an on-wrist wearable accelerometer,” pp. 1–28.Theodoridis, T., Solachidis, V., Vretos, N., & Daras, P. (2017). Human Fall Detection from Acceleration Measurements Using a Recurrent Neural Network. IFMBE Proceedings, 145-149. doi:10.1007/978-981-10-7419-6_25F. Sposaro and G. Tyson, “iFall : an android application for fall monitoring and response,” pp. 6119–6122, 2009.A. Ngu, Y. Wu, H. Zare, A.P. B, B. Yarbrough, and L. Yao, “Fall detection using smartwatch sensor data with accessor architecture,” vol. 2, pp. 81–93.P. Jantaraprim and P. Phukpattaranont, “Fall detection for the elderly using a support vector machine,” no. 1, pp. 484–490, 2012.Aziz, O., Musngi, M., Park, E. J., Mori, G., & Robinovitch, S. N. (2016). A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Medical & Biological Engineering & Computing, 55(1), 45-55. doi:10.1007/s11517-016-1504-yV. Carletti, A. Greco, A. Saggese, and M. Vento, “A smartphone-based system for detecting falls using anomaly detection,” vol. 6978, 2017, pp. 490–499.Yacchirema, D., de Puga, J. S., Palau, C., & Esteve, M. (2018). Fall detection system for elderly people using IoT and Big Data. Procedia Computer Science, 130, 603-610. doi:10.1016/j.procs.2018.04.11

Vision-based Human Fall Detection Systems using Deep Learning: A Review

Human fall is one of the very critical health issues, especially for elders and disabled people living alone. The number of elder populations is increasing steadily worldwide. Therefore, human fall detection is becoming an effective technique for assistive living for those people. For assistive living, deep learning and computer vision have been used largely. In this review article, we discuss deep learning (DL)-based state-of-the-art non-intrusive (vision-based) fall detection techniques. We also present a survey on fall detection benchmark datasets. For a clear understanding, we briefly discuss different metrics which are used to evaluate the performance of the fall detection systems. This article also gives a future direction on vision-based human fall detection techniques

Human fall detection on videos using convolutional neural networks with multiple channels

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Baixas taxas de mortalidade infantil, avanços na medicina e mudanças culturais aumentaram a expectativa de vida nos países desenvolvidos para mais de 60 anos. Alguns países esperam que, até 2030, 20% da sua população tenham mais de 65 anos. A qualidade de vida nessa idade avançada é altamente determinada pela saúde do indivíduo, que ditará se o idoso pode se engajar em atividades importantes para o seu bem estar, independência e satisfação pessoal. O envelhecimento é acompanhado por problemas de saúde causados por limitações biológicas e fraqueza muscular. Esse enfraquecimento facilita a ocorrência de quedas, responsáveis pela morte de aproximadamente 646.000 pessoas em todo o mundo e, mesmo quando uma pequena queda ocorre, ela ainda pode fraturar ossos ou danificar tecidos moles, que não cicatrizam completamente. Lesões e danos dessa natureza, por sua vez, podem afetar a autoconfiança do indivíduo, diminuindo sua independência. Neste trabalho, propomos um método capaz de detectar quedas humanas em sequências de vídeo usando redes neurais convolucionais (CNNs) multicanais. Nós desenvolvemos dois métodos para detecção de quedas, o primeiro utilizando uma CNN 2D e o segundo utilizando uma CNN 3D. Nossos métodos utilizam características extraídas previamente de cada quadro do vídeo e as classificam. Após a etapa de classificação, uma máquina de vetores de suporte (SVM) é aplicada para ponderar os canais de entrada e indicar se houve ou não uma queda. Experimentamos quatro tipos de características, a saber: (i) fluxo óptico, (ii) ritmo visual, (iii) estimativa de pose e (iv) mapa de saliência. As bases de dados utilizadas (URFD e FDD) estão disponíveis publicamente e nossos resultados são comparados com os da literatura. As métricas selecionadas para avaliação são acurácia balanceada, acurácia, sensibilidade e especificidade. Nossos métodos apresentaram resultados competitivos com os obtidos pelo estado da arte na base de dados URFD e superam os obtidos na base de dados FDD. Ao conhecimento dos autores, nós somos os primeiros a realizar testes cruzados entre os conjuntos de dados em questão, e a reportar resultados de acurácia balanceada. Os métodos propostos são capazes de detectar quedas nas bases selecionadas. A detecção de quedas, bem como a classificação de atividades em vídeos, está fortemente relacionada à capacidade da rede de interpretar informações temporais e, como esperado, o fluxo óptico é a característica mais relevante para a detecção de quedasAbstract: Lower child mortality rates, advances in medicine, and cultural changes have increased life expectancy in developed countries over 60 years old. Some countries expect that, by 2030, 20% of their population will be over 65 years old. The quality of life at this advanced age is highly dictated by the individual's health, which will determine whether the elderly can engage in important activities to their well-being, independence, and personal satisfaction. Old age is accompanied by health problems caused by biological limitations and muscle weakness. This weakening facilitates the occurrence of falls, which are responsible for the deaths of approximately 646,000 people worldwide and, even when a minor fall occurs, it can still cause fractures, break bones or damage soft tissues, which will not heal completely. Injuries and damages of this nature, in turn, will consume the self-confidence of the individual, diminishing their independence. In this work, we propose a method capable of detecting human falls in video sequences using multichannel convolutional neural networks (CNN). We developed two methods for fall detection, the first using a 2D CNN and the second using a 3D CNN. Our method uses features previously extracted from each frame and classifies them with a CNN. After the classification step, a support vector machine (SVM) is applied to weight the input channels and indicate whether or not there was a fall. We experiment with four types of features, namely: (i) optical flow, (ii) visual rhythm, (iii) pose estimation, and (iv) saliency map. The benchmarks used (URFD and FDD) are publicly available and our results are compared to those in the literature. The metrics selected for evaluation are balanced accuracy, accuracy, sensitivity, and specificity. Our results are competitive with those obtained by the state of the art on the URFD data set and surpass those on the FDD data set. To the authors' knowledge, we are the first to perform cross-tests between the datasets in question and to report results for the balanced accuracy metric. The proposed method is able to detect falls in the selected benchmarks. Fall detection, as well as activity classification in videos, is strongly related to the network's ability to interpret temporal information and, as expected, optical flow is the most relevant feature for detecting fallsMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Comprehensive review of vision-based fall detection systems

Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers