Activity detection is a key task in the monitoring of elderly people living alone. This is because it helps locate them and identify any accident that might occur to them. In this article, we propose a novel approach that uses 2-D light detection and ranging (LIDAR) and deep learning to perform activity detection. In a first step, our approach processes and interpolates the data collected using the 2-D LIDAR following an algorithm we propose to locate the person and identify the useful data points. In the next steps, the data are transformed into two types of representations: 1) a time-series type and 2) an image type. The time-series data are used to train different long short-term memory (LSTM) networks to identify the person and to recognize his/her activity, while the image type is used to fine-tune a convolutional neural network (CNN) for fall detection. Throughout our experiments, we show that our approach allows for the identification of people from their gait, and the detection of unsteady gait or unstable walk (i.e., when the person is about to fall or feeling dizzy) as well as the detection of up to four activities: 1) walking; 2) standing; 3) sitting; and 4) falling. The results obtained from our experiment show that the proposed method reaches an accuracy equal to 94.1% for multiclass activity detection, 98.6% for fall detection, 93.2% for person identification (for three different people), and 92.5% for unsteady walk detection.

In non-contact heart rate (HR) monitoring via Doppler radar, the disturbance from respiration and/or body motion is treated as a key problem on the estimation of HR. This paper proposes a blind source separation (BSS) approach to mitigate the noise effect in the received radar signal, and incorporates the sparse spectrum reconstruction (SSR) to achieve a high-resolution of heartbeat spectrum. The proposed BSS decomposes the spectrogram of mixture signal into original sources including heartbeat using non-negative matrix factorization (NMF) algorithms, through learning the complete basis spectra (BS) by a hierarchical clustering. In particular, to exploit the temporal sparsity of heartbeat component, two variants of NMF algorithms with sparseness constraints are applied as well, namely sparse NMF (SPNMF) and weighted sparse NMF (WSPNMF). Compared with usual BSS, our proposed BSS has three advantages: (i) Clustering-induced unsupervised manner; (ii) Compact demixing architecture; and (iii) Merely requiring signal-channel input data. In addition, the HR estimation method using our proposal delivers more satisfactory precision and robustness over other existing methods, which is demonstrated through the measurements to distinguishing people's activities, gaining both smallest absolute errors of HR estimation for sitting still and typewriting.

Heart rate (HR) variability indicates health condition and mental stress. The development of non-contact HR monitoring techniques with Doppler radar is attracting great attention. However, the performance of heartbeat detection via radar signals easily degrades due to respiration and body motion. In this paper, first, a stochastic gradient approach is applied to reconstruct the high-resolution spectrum of heartbeat by proposing the zero-attracting sign least-mean-square (ZA-SLMS) algorithm. To correct the quantized gradient of cost function and penalize the sparse constraint on updating the spectrum, a more accurate heartbeat spectrum is reconstructed. Then, to better adapt to the noises of different strengths caused by subjects' movements, an adaptive regularization parameter is introduced in the ZA-SLMS algorithm as an improved variant, which can adaptively regulate the proportion between gradient correction and sparse penalty. Moreover, in view of the stability of the location of the spectral peak associated with the HR when the size of time window slightly changes, a time-window-variation (TWV) technique is further incorporated in the improved ZA-SLMS (IZA-SLMS) algorithm for more stable HR estimation. Through the experiments on five subjects, our proposal is demonstrated to bring a significant improvement in accuracy compared with existing detection methods. Specifically, the IZA-SLMS algorithm with TWV achieves the smallest average error of 3.79 beats per minute when subjects type on a laptop.

Understanding the changes in choroidal thickness and vasculature is important to monitor the development and progression of various ophthalmic diseases. Accurate segmentation of the choroid layer and choroidal vessels is critical to better analyze and understand the choroidal changes. In this study, we develop a dense dilated U-Net model (ChoroidNET) for segmenting the choroid layer and choroidal vessels in optical coherence tomography (OCT) images. The performance of ChoroidNET is evaluated using an OCT dataset that contains images with various retinal pathologies. Overall Dice coefficient of 95.1 ± 0.4 and 82.4 ± 2.4 were obtained for choroid layer and vessel segmentation, respectively. Comparisons show that among state-of-the-art models, ChoroidNET, which produces results that are consistent with ground truths, is the most robust segmentation framework.

In this paper, we propose a method that uses low-resolution infrared (IR) array sensors to identify the presence and location of people indoors. In the first step, we introduce a method that uses 32 × 24 pixels IR array sensors and relies on deep learning to detect the presence and location of up to three people with an accuracy reaching 97.84%. The approach detects the presence of a single person with an accuracy equal to 100%. In the second step, we use lower end IR array sensors with even lower resolution (16 × 12 and 8 × 6) to perform the same tasks. We invoke super resolution and denoising techniques to faithfully upscale the low-resolution images into higher resolution ones. We then perform classification tasks and identify the number of people and their locations. Our experiments show that it is possible to detect up to three people and a single person with accuracy equal to 94.90 and 99.85%, respectively, when using frames of size 16 × 12. For frames of size 8 × 6, the accuracy reaches 86.79 and 97.59%, respectively. Compared to a much complex network (i.e., RetinaNet), our method presents an improvement of over 8% in detection.

Objective: The main challenge in contactless heartbeat detection comes from breathing and/or body motion, which typically deteriorate heart rate (HR) measurements, due to incorrect selection of spectral peaks associated with HR. To acquire the reliable peak selection on spectrum within a relatively broad range, this article first proposes a spectral Viterbi algorithm. Second, a nonlinear source separation approach is further proposed to eliminate the noises generated by respiration and movements, suppressing the undesired spectral energy. Proposal: Inspired by the fact that the period of peak-to-peak intervals of heartbeat (RRIs) rarely vary within a short duration, a novel spectral Viterbi algorithm is proposed to estimate HR change, by the path metric (PM) of candidate paths of HR change. Moreover, based on a deep recurrent neural network (RNN), deep clustering (DC) is applied to separate out the targeted heartbeat source from Doppler signal, by dividing its spectrogram. Results: On the premise of wide-range HR measurement, the usage of spectral Viterbi algorithm substantially improved the precision compared with typical methods of HR estimation, both in the statuses of human subjects' sitting still and typewriting. In addition, the combination of DC obtains the smallest average errors. Significance: The proposed spectral Viterbi algorithm with DC is provided with three main strengths: 1) good adaptability to wide-range HR change; 2) robustness to nonlinearly mixed signal and noises; and 3) requirement of only a single-channel sensor.