Jul 2026· 2026 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv)· pp. 151-156· 0 citations· 16 references
Abstract
The accurate measurement of physical activity in indoor environments is crucial for Ambient Assisted Living (AAL) applications, yet current wearable and vision-based technologies suffer from user obtrusiveness and privacy concerns. To overcome these limitations, this paper proposes a privacy-preserving activity measurement framework based on a single ceiling-mounted Time-of-Flight (ToF) ultrasonic sensor. An experimental campaign was conducted involving 20 healthy subjects performing 5 different activities in a Living Lab. The proposed approach uses a time-frequency analysis by applying the Continuous Wavelet Transform (CWT) to raw one-dimensional distance signals. This process generates detailed time-frequency scalograms, from which the total sum of the wavelet power spectrum is computed as the primary quantitative feature to discriminate the activities. A statistical analysis using a Repeated-Measures ANOVA followed by post-hoc paired t-tests with Bonferroni correction demonstrated that the extracted metrological feature successfully discriminates between activities belonging to different physiological intensity zones. Specifically, the squat activity was successfully distinguished from all other activities $(p<0.05)$. The resting baseline was significantly different from cleaning, sweeping, and squats, although its difference with the filing documents activity was not statistically significant $(p=0.121)$, given the highly static nature of the latter. These results demonstrate that an advanced time-frequency analysis of ultrasonic distance measurements provides a reliable indicator for Human Activity Recognition (HAR), offering a highly effective alternative to black-box models for smart home monitoring.
Radio Frequency (RF) sensing offers a completely novel and non-contact approach by exploiting RF reflections from, and through, the body for detecting small respiratory motions. The current study has used RF-based Software Defined Radio Frequency (SDRF) sensing to detect various breathing rates, including fast, normal, and shortness of breath. A correlation matrix and standard deviation analysis of 146 OFDM (Orthogonal Frequency Division Multiplexing) subcarriers was performed to determine signal variability and consistency. Machine learning-based results subsequently show that cleaned data improve subcarrier correlation uniformity, with an enhanced focus on normal breathing, while still maintaining signal features related to all breathing modes. Subcarrier selection, filtering and normalization strengthen the accuracy of the obtained data as the preprocessing stages eliminate the noises and artifacts. This paper demonstrates the reliability of Radio Frequency (RF)-based systems for respiratory monitoring, as well as the possibility of extracting highly detailed features relevant to developing more complex, real-time healthcare solutions.
Qurat Ul Ain, R. Asif, Nan Zhao et al.· E3S Web of Conferences· 0 citations
Respiratory rate (RR) is a vital physiological marker whose accurate and continuous monitoring holds particular clinical significance. Conventional contact-based sensors, while reliable, impose physical constraints that may cause discomfort and skin irritation. Radar-based sensing, particularly frequency-modulated continuous-wave (FMCW) radar, offers a compelling contactless alternative: it operates through clothing, remains unaffected by lighting conditions, and enables unobtrusive continuous monitoring without compromising patient comfort or privacy. Nevertheless, radar-based RR estimation remains technically challenging due to signal noise, motion artifacts, and the inherent subtlety and variability of breathing patterns. Although prior work has demonstrated viable RR estimation using either signal processing or machine learning approaches, a direct comparison of both paradigms on pediatric radar data under a unified preprocessing framework has not been established. This study addresses that gap through a systematic and comprehensive comparative evaluation of classical signal processing and machine learning (ML) techniques for RR estimation using a publicly available FMCW radar dataset collected from 50 children under 13 years old. Six methods were evaluated: fast Fourier transform (FFT)-based frequency-domain analysis, time-domain peak detection, convolutional neural networks (CNN), multilayer perceptron (MLP), LightGBM regression, and KalmanNet. All were benchmarked against the ground truth obtained from a clinically validated reference system. The frequency-domain analysis method combined with multichannel averaging achieved the best overall performance, with a mean absolute error (MAE) of 2.60 breaths per minute (bpm), a root mean square error (RMSE) of 3.39 bpm, and an average inference time of 44.2 ms per analysis window, outperforming time-domain peak detection (MAE 3.66 bpm), CNN (MAE 2.75 bpm, RMSE 3.39 bpm), MLP (MAE 4.01 bpm, RMSE 4.64 bpm), LightGBM (MAE 5.91 bpm, RMSE 6.41 bpm), and KalmanNet (MAE 5.97 bpm, RMSE 6.73 bpm). These results indicate that, in the limited-data pediatric setting, a carefully designed classical signal processing method provides highly competitive performance, achieving accuracy statistically comparable to CNN while outperforming the remaining handcrafted feature-based learning methods, and remaining computationally efficient and interpretable.
Nur Ahmadi, H. V. Tanoto, Diyah Widiyasari et al.· IEEE Access· 0 citations
With the expansion of the companion-animal market, animal protection, health management and harmonious coexistence between animals and humans have become increasingly important. The source project designs a portable embedded vital-sign monitoring system based on millimeter-wave radar. The system uses the IWR6843AOPEVM FMCW radar to collect raw physiological data, extracts heart-rate and respiration information through signal processing, displays the results on an OLED module, and transmits the data wirelessly to an upper computer such as a smartphone. Three domestic Doberman Pinschers were selected as test subjects, and heart-rate and respiration data were collected across daily states. Non-parametric testing, Spearman correlation, decision-tree classification, reliability analysis and validity analysis were used to evaluate the experimental data and product feedback. The condensed results show that the system has non-contact, real-time and portable monitoring advantages. Heart rate is the main basis for state classification, the decision-tree model performs well, and questionnaire analysis indicates that continuous working time and portability are the user concerns that require further optimization.
This paper presents a smartphone-based sonar-sensing system for contactless monitoring of respiration and gesture-based user activity, using a single commercial phone with no hardware modification. A 20 kHz audio signal is transmitted from the phone’s speaker and reflections are captured by its microphone; motion-induced Doppler shifts are recovered using an envelope detection method that removes the need for a reference copy of the transmitted signal and improves robustness to transmitter frequency drift. Actuator experiments at a target distance around 50 cm demonstrated motion sensitivity at two conditions, 6 mm at 0.15 Hz and 2 mm at 0.6 Hz, with a measured signal-to-noise ratio of approximately 21.7 dB. Respiration rate detected by the system was compared against a pulse oximeter across two subjects, with an error within 0.1 Hz of the reference. Gesture-based interactions (tapping, scrolling, typing, talking) were analyzed using short-time Fourier transform features as a first step toward device authentication. Distance and direction estimation are outside the scope of this work and are identified as directions for future development.
Raj Nandini, Ashish Mishra· Italian National Conference...· 0 citations
A novel activity intensity score (AIS) framework that provides a nonintrusive and continuous measure of activity intensity by analyzing video data and enables robust, real-time measurement of movement intensity for applications ranging from healthcare and workplace ergonomics to sports analytics and adaptive HVAC control.
Moein Younesi Heravi, Inbae Jeong, Youjin Jang· Journal of computing in civi...· 0 citations
Non-contact vital sign monitoring using millimeter-wave radar has emerged as a promising alternative to contact-based devices for continuous healthcare and elderly care applications. However, accurate heart rate estimation remains challenging because the weak cardiac-induced chest displacement is approximately an order of magnitude smaller than respiratory motion, and its fundamental frequency is frequently masked by higher-order respiratory harmonics. Here we propose a signal processing framework that addresses this challenge through three integrated stages: a slow-time phase correlation method that enhances the signal-to-noise ratio by coherently aggregating vital sign energy from adjacent range bins; an adaptive harmonic matching filtering approach based on complementary ensemble empirical mode decomposition that isolates and suppresses respiratory harmonic interference; and autocorrelation-based heart rate estimation. Experimental results obtained with a 77 GHz FMCW radar demonstrate that the proposed method achieves heart rate estimates within 5% error of reference wearable sensors in the presence of respiratory harmonics, with robustness confirmed through long-duration testing. This framework provides a practical solution for reliable radar-based heart rate monitoring without requiring subject-specific calibration or specialized hardware modifications.
Di-Di Xu, Ying Li, Zi-Nan Wu et al.· Italian National Conference...· 0 citations
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