Fall detection is a major challenge for researchers. Indeed, a fall can cause injuries such as femoral neck fracture, brain hemorrhage, or skin burns, leading to significant pain. However, in some cases, trauma caused by an undetected fall can get worse with the time and conducts to painful end of life or even death. One solution is to detect falls efficiently to alert somebody (e.g., nurses) as quickly as possible. To respond to this need, we propose to detect falls in a real apartment of 40 square meters by exploiting three ultra-wideband radars and a deep neural network model. The deep neural network is composed of a convolutional neural network stacked with a long-short term memory network and a fully connected neural network to identify falls. In other words, the problem addressed in this paper is a binary classification attempting to differentiate fall and non-fall events. As it can be noticed in real cases, the falls can have different forms. Hence, the data to train and test the classification model have been generated with falls (four types) simulated by 10 participants in three locations in the apartment. Finally, the train and test stages have been achieved according to three strategies, including the leave-one-subject-out method. This latter method allows for obtaining the performances of the proposed system in a generalization context. The results are very promising since we reach almost 90% of accuracy.

Fall Detection With UWB Radars and CNN-LSTM Architecture

Abstract Compressive sensing (CS) is a mathematically elegant tool for reducing the sensor sampling rate, potentially bringing context-awareness to a wider range of devices. Nevertheless, practical issues with the sampling and reconstruction algorithms prevent further proliferation of CS in real world domains, especially among heterogeneous ubiquitous devices. Deep learning (DL) naturally complements CS for adapting the sampling matrix, reconstructing the signal, and learning from the compressed samples. While the CS–DL integration has received substantial research interest recently, it has not yet been thoroughly surveyed, nor has any light been shed on practical issues towards bringing the CS–DL to real world implementations in the ubiquitous computing domain. In this paper we identify main possible ways in which CS and DL can interplay, extract key ideas for making CS–DL efficient, outline major trends in the CS–DL research space, and derive guidelines for the future evolution of CS–DL within the ubiquitous computing domain. 

https://link.springer.com/content/pdf/10.1007/s10462-022-10259-5.pdf

Deep learning for compressive sensing: a ubiquitous systems perspective

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Elderly Fall Detection and Fall Direction Detection via Various Machine Learning Algorithms Using Wearable Sensors

Compressive sensing (CS) is a promising technology for realizing energy-efficient wireless sensors for long-term health monitoring. However, conventional model-driven CS frameworks suffer from limited compression ratio and reconstruction quality when dealing with physiological signals due to inaccurate models and the overlook of individual variability. In this paper, we propose a data-driven CS framework that can learn signal characteristics and personalized features from any individual recording of physiologic signals to enhance CS performance with a minimized number of measurements. Such improvements are accomplished by a co-training approach that optimizes the sensing matrix and the dictionary towards improved restricted isometry property and signal sparsity, respectively. Experimental results upon ECG signals show that the proposed method, at a compression ratio of 10x, successfully reduces the isometry constant of the trained sensing matrices by 86% against random matrices and improves the overall reconstructed signal-to-noise ratio by 15dB over conventional model-driven approaches.

A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing

The success of machine learning (ML) as well as deep learning (DL) depends largely on data availability and quality. The system’s performance is frequently more affected by the amount and quality of its training data than by its architecture and training specifics. Consequently, demand exists for challenging datasets that both precisely measure performance and present unique challenges with real-world applications. The Egypt Monuments Dataset v1 (EGYPT-v1) is introduced as a new scalable benchmark for fine-image classification (IC) and object recognition (OR) in the domain of ancient Egyptian monuments. EGYPT-v1 dataset is by far the world’s first large specified such dataset to date, with over seven thousand images and 40 distinct instance labels. The dataset composes different categories of monuments such as pyramids, temples, mummies, statues, head statues, bust statues, heritage sites, palaces and shrines. Several advanced deep network architectures were tested to appraise the classification difficulty in the EGYPT-v1 dataset, namely ResNet50, Inception V3, and LeNet5 models. The models achieved accuracy rates as follows: 99.13%, 90.90%, and 92.64%, respectively. The dataset was predominantly created by manually collecting images from the popular global online video-sharing and social media platform, Youtube, as well as WATCHiT, Egypt’s top streaming entertainment service. Additionally, Wikimedia Commons, the largest crowdsourced media repository in the world, was used as a secondary source of images. The images that comprise the dataset can be accessed on the GitHub repository https://github.com/mennatallahhassan/egyptmonuments-dataset. Keywords—Deep learning; landmark datasets; landmark recognition; monument datasets; monument recognition

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Egypt Monuments Dataset version 1: A Scalable Benchmark for Image Classification and Monument Recognition

Fall detection holds immense importance in the field of healthcare, where timely detection allows for instant medical assistance. In this context, we propose a 3D ConvNet architecture which consists of 3D Inception modules for fall detection. The proposed architecture is a custom version of Inflated 3D (I3D) architecture, that takes compressed measurements of video sequence as spatio-temporal input, obtained from compressive sensing framework, rather than video sequence as input, as in the case of I3D convolutional neural network. This is adopted since privacy raises a huge concern for patients being monitored through these RGB cameras. The proposed framework for fall detection is flexible enough with respect to a wide variety of measurement matrices. Ten action classes randomly selected from Kinetics-400 with no fall examples, are employed to train our 3D ConvNet post compressive sensing with different types of sensing matrices on the original video clips. Our results show that 3D ConvNet performance remains unchanged with different sensing matrices. Also, the performance obtained with Kinetics pre-trained 3D ConvNet on compressively sensed fall videos from benchmark datasets is better than the state-of-the-art techniques.

Compressive sensing based privacy for fall detection.

Compressive sensing based privacy for fall detection

Tasks such as action recognition requires high quality features for accurate inference. But the use of high resolution and large volume of video data poses a significant challenge for inference in terms of storage and computational complexity. In addition, compressive sensing as a potential solution to the aforementioned problems has been shown to recover signals at higher compression ratios with loss in information. Hence, a framework is required that performs good quality action recognition on compressively sensed data. In this paper, we present data-driven sensing for spatial multiplexers trained with combined mean square error (MSE) and perceptual loss using Deep convolutional neural networks. We employ subpixel convolutional layers with the 2D Convolutional Encoder-Decoder model, that learns the downscaling filters to bring the input from higher dimension to lower dimension in encoder and learns the reverse, i.e. upscaling filters in the decoder. We stack this Encoder with Inflated 3D ConvNet and train the cascaded network with cross-entropy loss for Action recognition. After encoding data and undersampling it by over 100 times (10 \(\times \) 10) from the input size, we obtain 75.05% accuracy on UCF-101 and 50.39% accuracy on HMDB-51 with our proposed architecture setting the baseline for reconstruction free action recognition with data-driven sensing using deep learning. We experimentally infer that the encoded information from such spatial multiplexers can directly be used for action recognition.

Data Driven Sensing for Action Recognition Using Deep Convolutional Neural Networks

Monument classification can be performed on the basis of their appearance and shape from coarse to fine categories, although there is much semantic information present in the monuments which is reflected in the eras they were built, its type or purpose, the dynasty which established it, etc. Particularly, Indian subcontinent exhibits a huge deal of variation in terms of architectural styles owing to its rich cultural heritage. In this paper, we propose a framework that utilizes hierarchy to preserve semantic information while performing image classification or image retrieval. We encode the learnt deep semantic embeddings to construct a dictionary of images and then utilize a re-ranking framework on the the retrieved results using DeLF features. The semantic information preserved in these embeddings helps to classify unknown monuments at higher level of granularity in hierarchy. We have curated a large, novel Indian heritage monuments dataset (IHMD) comprising of images of historical, cultural and religious importance with subtypes of eras, dynasties and architectural styles. We demonstrate the performance of the proposed framework in image classification and retrieval tasks and compare it with other competing methods on this dataset. The dataset can be accessed at: https://github.com/ronakgupta/HierarchicalSemanticsEmbeddings.

Semantics Preserving Hierarchy based Retrieval of Indian heritage monuments

Compressive sensing (CS) using deep learning for recovery of images from measurements has been well explored in recent years. Instead of sensing/sampling full image, block or patch based compressive sensing is chosen to overcome memory and computation limitations. The drawback of this block based CS sampling and recovery is that it does not capture global context and focuses only on the local context. This results in artifacts at the boundary of two consecutive image blocks. Random Gaussian or random Bernoulli matrix are commonly used as sensing matrices to sample an image block and generate corresponding linear measurements. Although, random Gaussian or random Bernoulli matrices exhibits Restricted Isometry property (RIP), which is a guarantee for good quality reconstructed image, its two main disadvantages are: 1) large memory and computational requirements and 2) their encoded measurements doesn't generalize well to a large-scale dataset. In this paper, we propose a data adaptive CS based on deep learning framework for image recognition where 1) sampling is done considering the global context and 2) encoding to obtain measurements is learned from data, so as to achieve the generalization over large-scale dataset.

Ronak Gupta

Papers

Compressive sensing based privacy for fall detection.

Compressive sensing based privacy for fall detection

Data Driven Sensing for Action Recognition Using Deep Convolutional Neural Networks

Semantics Preserving Hierarchy based Retrieval of Indian heritage monuments

Data Adaptive Compressed Sensing using deep neural network for Image recognition