A unified methodology for heartbeats detection in seismocardiogram and ballistocardiogram signals

This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets (p > 0.05 from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found (p < 0.01) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors

Detection and analysis of heartbeats in seismocardiogram signals

This paper presents an unsupervised methodology to analyze SeismoCardioGram (SCG) signals. Starting from raw accelerometric data, heartbeat complexes are extracted and annotated, using a two-step procedure. An unsupervised calibration procedure is added to better adapt to different user patterns. Results show that the performance scores achieved by the proposed methodology improve over related literature: on average, 98.5% sensitivity and 98.6% precision are achieved in beat detection, whereas RMS (Root Mean Square) error in heartbeat interval estimation is as low as 4.6 ms. This allows SCG heartbeat complexes to be reliably extracted. Then, the morphological information of such waveforms is further processed by means of a modular Convolutional Variational AutoEncoder network, aiming at extracting compressed, meaningful representation. After unsupervised training, the VAE network is able to recognize different signal morphologies, associating each user to its specific patterns with high accuracy, as indicated by specific performance metrics (including adjusted random and mutual information score, completeness, and homogeneity). Finally, a Linear Model is used to interpret the results of clustering in the learned latent space, highlighting the impact of different VAE architectural parameters (i.e., number of stacked convolutional units and dimension of latent space)

An unsupervised behavioral modeling and alerting system based on passive sensing for elderly care

Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects’ health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia–Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects’ daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient’s behavior as a ‘Bag of Words’, based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects’ daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort

Sensitivity Of Reliability Growth Models To Operational Profile Errors

The estimation of the operational profile is one of the key factors during the use of software reliability growth models. But, the operational profile can be very difficult to estimate in particular applications such as the one of software used for process control. In other cases, a single operational profile may not be sufficient to describe the use of the product by a number of different customers. An operational profile may also change during the development of software or during its operational usage. All these cases may lead to errors in the estimation of the operational profile. The paper describes an empirical evaluation of the sensitivity of reliability growth models to errors in the estimation of the operational profiles. Some reliability growth models are applied during the testing phase of a software system. The particular characteristics of the case study allow the measurement of the actual reliability growth of the software and its comparison with the estimations provided by the models. Measurement and comparison are repeated for different operational profiles giving information about the effect of a possible error in the estimation of the operational profile. Results show that errors in the operational profile estimation do not heavily affect reliability estimates and that their influence is strongly dependent on the accuracy with which the software system has been tested.354

Sensitivity of reliability-growth models to operational profile errors vs testing accuracy

This paper investigates: 1) the sensitivity of reliability-growth models to errors in the estimate of the operational profile (OP), and 2) the relation between this sensitivity and the testing accuracy. The investigation is based on the results of a case study in which several reliability-growth models are applied during the testing phase of a software system. The faults contained in the system are known in advance; this allows measurement of the software reliability-growth and comparison with the estimates provided by the models, Measurement & comparison are repeated for various OP, thus giving information about the effect of a possible error in the estimate of the OP. The results show that: 1) the predictive accuracy of the models is not heavily affected by errors in the estimate of the OP, and 2) this relation depends on the accuracy with which the software system has been tested.45453154

Seismocardiography-based detection of heartbeats for continuous monitoring of vital signs

This paper presents an automated procedure for analyzing SeismoCardioGraphic (SCG) traces, i.e. chest vibrations induced by heart activity. Such signals are acquired by means of an inexpensive, compact accelerometer device, placed over the subject's sternum and held in place by a light strap. The methodology for beat detection and annotation features a preliminary coarse beat detection phase, followed by a self-managed calibration, that is subsequently leveraged to carry out actual SCG annotation and beat localization. The performance of such process was measured and compared against the ECG gold standard, used only to provide ground-truth beat-to-beat intervals (i.e. R-peak landmarks). Results show high average values of sensitivity and precision (99.1% and 97.9%, respectively). The coefficient of determination R2 reaches a 0.984 value, which shows that the algorithm is able to temporally localize SCG complexes in a very consistent way, compared to the ECG gold standard; indeed, the variability between the ECG and SCG beat to beat measures is found to be very limited (σdiff ≈ 8.5 ms, i.e. less than one sample interval)

Accurate annotation of SeismoCardioGram exploiting multi-dimensional accelerometry

This paper presents a methodology to assess heartrelated parameters (such as heart rate and heart rate variability) from SeismoCardioGram (SCG) signals acquired with an Inertial Measurement Unit (IMU). Specific SCG landmarks are detected and identified, including Aortic valve Opening (AO) and Isovolumetric Moment (IM) phases. In order to improve detection accuracy and robustness, information from all 3 accelerometer's axes are combined. Preliminary results are promising, highlighting good overall linearity (r 2 > 0.965) with respect to reference ECG-based (ElectroCardioGram) measurements. By considering all 3 accelerometer axes, performance improves over single-axis approaches. Results suggest the feasibility of accurate, continuous measurements of heart rate and its variability (in restconditions) in AAL scenarios, where such information may effectively complement physical activity and activity intensity data

Exploiting AAL Environment for Behavioral Analysis

Recent environmental control systems, designed and developed according to Ambient Assisted Living paradigm, can increase the chances of active and independent living for elderly people and people with disabilities, by improving their safety and autonomy. Such systems can play an assistive role, interacting with users through non-invasive smart-sensors, monitoring their wellbeing and implementing behavioural analysis. This paper aims to present some tools developed into the CARDEA AAL system to foster a more effective transfer of AAL outcome to caregiving practices. By processing raw data generated from low-cost environmental sensors, CARDEA allows for the identification of behavioural “trends”, which can be meaningful to caregivers. Presentation tools are proposed, suitable for highlighting relevant issues, the actual interpretation of which is left to the caregiver