11 research outputs found
Fall prediction in hypertensive patients via short-term HRV analysis
Falls are a major problem of later life having severe consequences on quality of life and a significant burden in occidental countries. Many technological solutions have been proposed to assess the risk or to predict falls and the majority is based on accelerometers and gyroscopes. However, very little was done for identifying first time fallers, which are very difficult to recognise. This paper presents a meta-model predicting falls using short term Heart Rate Variability (HRV) analysis acquired at the baseline. 170 hypertensive patients (age: 72 ± 8 years, 56 female) were investigated, of which 34 fell once in the 3 months after the baseline assessment. This study is focused on hypertensive patients, which were considered as convenient pragmatic sample, as they undergo regular outpatient visits, during which short term ECG can be easily recorded without significant increase of healthcare costs. For each subject, 11 consecutive excerpts of 5 minutes each (55 min) were extracted from ECGs recorded between 10:30 and 12:30 and analysed. Linear and nonlinear HRV features were extracted and averaged among the 11 excerpts, which were, then, considered for the statistical and data mining analysis. The best predictive meta-model was based on Multinomial Naïve Bayes, which enabled to predict first-time fallers with sensitivity, specificity and accuracy rates of 72%, 61%, 68% respectively
Pengecaman peristiwa jatuh secara tiba-tiba menggunakan fitur gerakan dan pengelas ilhaman biologi sistem penglihatan
Kajian tentang pengecaman peristiwa yang berlaku secara tiba-tiba untuk sistem video
pengawasan dikenal pasti boleh menyumbang ke arah pengurangan kos pembangunan
teknologi sistem peranti pengesan bolehpakai dan juga ketidakselesaan pemakainya.
Adalah dijangkakan, populasi penduduk dunia akan bertambah pada masa akan datang
ekoran peningkatan jangka hayat manusia yang menyebabkan peningkatan bilangan
penduduk dunia berumur 60 tahun ke atas. Oleh itu, sistem penjagaan keselamatan
penghuni dalam rumah tak invasif yang boleh berfungsi untuk mengawas dan
mengesan sebarang kejadian kemalangan yang tidak diingini seperti rebah, pengsan
dan lain-lain akan menjadi penting dan berguna untuk warga tua khususnya untuk
mereka yang tinggal bersendirian. Perkembangan dalam sistem pengecaman peristiwa
yang berlaku secara tiba-tiba dijangkakan dapat menyediakan kemudahan kepada
warga tua yang tinggal bersendirian di samping berupaya menjaga keselamatan
mereka di rumah. Ini akan dapat mengurangkan kos perbelanjaan di pusat jagaan
warga tua. Justeru, objektif utama kajian adalah untuk membangunkan satu kaedah
mengesan gerakan dan mengecam peristiwa yang berlaku secara tiba-tiba dan
memerlukan tindakan serta perhatian segera. Perlaksanaan pembangunan kaedah
pengecaman kejadian melibatkan tiga langkah penting iaitu, pemprosesan awal,
penyarian fitur dan pengelasan. Pemprosesan awal menggunakan teknik penolakan
latar belakang (PLB) dan teknik pelicinan, (penuras kebarangkalian ruang, SPF dan
sokongan data kejiranan, NDS) untuk mengurangkan hingar imej bebayang objek.
Sifat gerakan telah dikenalpasti sebagai salah satu sifat yang penting dan relevan bagi
mengesan perubahan mendadak pada orientasi, arah dan penampilan objek dalam
sesebuah jujukan video. Terdapat tiga kaedah sarian fitur gerakan yang berasaskan
ruang-masa iaitu templat, aliran vektor gerakan (AVG) dan ilhaman biologi sistem
penglihatan manusia telah dilaksanakan. Seterusnya, keberkesanan fitur gerakan diuji
dengan menggunakan tiga pengelas sedia ada iaitu k-kejiranan terdekat (k-NN), mesin
vektor sokongan (SVM) dan rangkaian neural inspirasi biologi suap hadapan
(BFFNN-P). Potensi pengelas BFFNN-P untuk mengelas peristiwa jatuh berbanding
dengan aktiviti harian yang lain ditingkatkan melalui kaedah kawalan ralat berkadar
(P), kamiran (I) dan terbitan (D). Hasil kajian yang diperolehi menunjukkan teknik
SPF telah memberikan keputusan yang baik dalam mengurangkan hingar dan
melicinkan imej bebayang objek. Fitur gerakan GaussH yang berasaskan inspirasi
sistem penglihatan manusia telah memberikan keputusan yang lebih baik berbanding
templat dan AVG dengan menggunakan pengelas BFFNN-PD. Prestasi kejituan,
kepekaan dan kepekaan bagi fitur gerakan GaussH dengan pengelas BFFNN-PD
adalah 98.6%, 98.2% dan 99.5%. Kesimpulannya, penyelidikan ini telah berjaya
menghasilkan kaedah pengelasan melalui pendekatan inspirasi biologi yang mampu
mengesan peristiwa yang berlaku secara tiba-tiba
mmFall: Fall Detection using 4D MmWave Radar and a Hybrid Variational RNN AutoEncoder
In this paper we propose mmFall - a novel fall detection system, which
comprises of (i) the emerging millimeter-wave (mmWave) radar sensor to collect
the human body's point cloud along with the body centroid, and (ii) a
variational recurrent autoencoder (VRAE) to compute the anomaly level of the
body motion based on the acquired point cloud. A fall is claimed to have
occurred when the spike in anomaly level and the drop in centroid height occur
simultaneously. The mmWave radar sensor provides several advantages, such as
privacycompliance and high-sensitivity to motion, over the traditional sensing
modalities. However, (i) randomness in radar point cloud data and (ii)
difficulties in fall collection/labeling in the traditional supervised fall
detection approaches are the two main challenges. To overcome the randomness in
radar data, the proposed VRAE uses variational inference, a probabilistic
approach rather than the traditional deterministic approach, to infer the
posterior probability of the body's latent motion state at each frame, followed
by a recurrent neural network (RNN) to learn the temporal features of the
motion over multiple frames. Moreover, to circumvent the difficulties in fall
data collection/labeling, the VRAE is built upon an autoencoder architecture in
a semi-supervised approach, and trained on only normal activities of daily
living (ADL) such that in the inference stage the VRAE will generate a spike in
the anomaly level once an abnormal motion, such as fall, occurs. During the
experiment, we implemented the VRAE along with two other baselines, and tested
on the dataset collected in an apartment. The receiver operating characteristic
(ROC) curve indicates that our proposed model outperforms the other two
baselines, and achieves 98% detection out of 50 falls at the expense of just 2
false alarms.Comment: Preprint versio
Fall Detection by Using Video
Cameras have become common in our society and as a result there is more video available today than ever before. While the video can be used for entertainment or possibly as storage it can also be used as a sensor capturing crucial information, The information captured can be put to all types of uses, but one particular use is to identify a fall. The importance of identifying a fall can be seen especially in the older population that is affected by falls every year. The falls experienced by the elderly are devastating as they can cause apprehension to normal life activities and in some cases premature death. Another fall related issue is the intentional deception in a business with intent of insurance fraud. Classification algorithms based on video can be constructed to detect falls and separate them as either accidental or intentional. This thesis proposes an algorithm based on frame segmentation, and speed components in the x, y, z directions over time t. The speed components are estimated from the video of orthogonally positioned cameras. The algorithm can discern between fall activities and others like sitting on the floor, lying on the floor, or exercising
Tag-free indoor fall detection using transformer network encoder and data fusion
This work presents a radio frequency identification (RFID)-based technique to detect falls in the elderly. The proposed RFID-based approach offers a practical and efficient alternative to wearables, which can be uncomfortable to wear and may negatively impact user experience. The system utilises strategically positioned passive ultra-high frequency (UHF) tag array, enabling unobtrusive monitoring of elderly individuals. This contactless solution queries battery-less tag and processes the received signal strength indicator (RSSI) and phase data. Leveraging the powerful data-fitting capabilities of a transformer model to take raw RSSI and phase data as input with minimal preprocessing, combined with data fusion, it significantly improves activity recognition and fall detection accuracy, achieving an average rate exceeding 96.5%. This performance surpasses existing methods such as convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM), demonstrating its reliability and potential for practical implementation. Additionally, the system maintains good accuracy beyond a 3-m range using minimal battery-less UHF tags and a single antenna, enhancing its practicality and cost-effectiveness
Automatic risk evaluation in elderly patients based on Autonomic Nervous System assessment
Dysfunction of Autonomic Nervous System (ANS) is a typical feature of chronic heart failure and other cardiovascular disease. As a simple non-invasive technology, heart rate variability (HRV) analysis provides reliable information on autonomic modulation of heart rate. The aim of this thesis was to research and develop automatic methods based on ANS assessment for evaluation of risk in cardiac patients. Several features selection and machine learning algorithms have been combined to achieve the goals.
Automatic assessment of disease severity in Congestive Heart Failure (CHF) patients: a completely automatic method, based on long-term HRV was proposed in order to automatically assess the severity of CHF, achieving a sensitivity rate of 93% and a specificity rate of 64% in discriminating severe versus mild patients.
Automatic identification of hypertensive patients at high risk of vascular events: a completely automatic system was proposed in order to identify hypertensive patients at higher risk to develop vascular events in the 12 months following the electrocardiographic recordings, achieving a sensitivity rate of 71% and a specificity rate of 86% in identifying high-risk subjects among hypertensive patients.
Automatic identification of hypertensive patients with history of fall: it was explored whether an automatic identification of fallers among hypertensive patients based on HRV was feasible.
The results obtained in this thesis could have implications both in clinical practice and in clinical research. The system has been designed and developed in order to be clinically feasible. Moreover, since 5-minute ECG recording is inexpensive, easy to assess, and non-invasive, future research will focus on the clinical applicability of the system as a screening tool in non-specialized ambulatories, in order to identify high-risk patients to be shortlisted for more complex investigations