Wearable Sensors Research Articles

Self-Powered, Flexible, Wireless and Intelligent Human Health Management System Based on Natural Recyclable Materials.

Combining wearable sensors with modern technologies such as internet of things and big data to monitor or intervene in obesity-induced chronic diseases, such as obstructive sleep apnea, type II diabetes, cardiovascular diseases, and Alzheimer's disease, is of great significance to the self-health management of human beings. This study designed a loofah-conducting graphite four friction layer enhanced triboelectric nanogenerator (LG-TENG) and developed a health management system for human motion recognition and early warning of sleep breathing abnormalities. By uniformly spraying and depositing conductive graphite on the surface of the loofah and the elastic film cross-interlocking bending structure design, the signal strength of the LG-TENG has been improved by 390%. The stable output signal is still maintained after 1500 s of continuous operation at a frequency of 2 Hz. LG-TENG can realize accurate motion analysis by muscle contraction state. Combining different deep learning models resulted in 98.1% accuracy in recognizing seven categories of displacement speeds for an individual and 96.46% accuracy in recognizing seven categories of displacement speeds for three individuals. In addition, the sleep breathing monitoring early warning system was developed by integrating Bluetooth wireless transmission and upper computer analysis technology. This system aims to analyze and provide real-time warnings for sleep-breathing abnormalities. This research promotes an innovation of TENG technology based on the advantages of natural materials, recyclability and low cost. It offers new ideas for self-health management and scientific exercise for obese people, showing a broad application prospect.

Gait Analysis of Elderly and Young People Based on Joint Angles and Positions

For the elderly, the motor function of walking is significant for the maintenance and improvement of their quality of life (QOL). Walking patterns have been postulated to be associated with a decline in walking ability. The objective of this study was to develop a wearable sensor that can capture changes in walking ability in daily life. To this end, motion capture was employed to measure the lower limb joint angles and the trajectories of the lower limbs and knees for each subject while walking at a normal walking speed and to compare the results between elderly and young patients. The differences in walking patterns between the elderly and young patients were analyzed in terms of elevation angles, and an index was calculated to capture changes during walking based on elevation angles and trajectories.

Preparation of PAMgA/GL/GO antifreeze conductive hydrogel for wearable strain sensor based on physically cross-linked network

Conductive hydrogels have promising prospects in wearable strain sensor applications. However, the complex and diverse application scenarios put forward higher requirements on the conductivity, anti-freezing, tensile, self-healing, adhesion, and moisture retention properties of the conductive hydrogel. In this study, a polymagnesium acrylate/glycerol/graphene oxide (PAMgA/GL/GO) conductive hydrogel is prepared by free radical polymerization method, with magnesium acrylate (AMgA) as the polymer monomer, glycerol (GL) as the antifreeze filler, and graphene oxide (GO) as the conductive filler. Adding GL not only enhances the antifreeze properties of the composite hydrogels but also improves the moisture retention. The addition of GO increases the conductivity and the gauge factor (GF) of the composite hydrogels. When the GO content is 0.6 wt.%, the PAMgA/GL/GO conductive hydrogel demonstrates excellent properties of antifreeze, conductivity, self-healing, adhesion, and sensitivity. These excellent properties make it suitable as a wearable strain sensor for accurately monitoring human joint movements and subtle physiological signals.

Self‐Powered Wearable Pressure Sensors for Detection and Separation of Signals for Various Human Movements

A detailed study on the dynamic response of mountable pressure sensors is presented, with a focus on foot pressure sensors integrated with carbon nanotube (CNT)‐coated cotton fibers. The research explores the sensor's sensitivity to pressure changes, repeatability, hysteresis, and durability through rigorous modeling and experimental validation. Computational simulations using Python and experimental data demonstrate the sensor's nonlinear conductance response to applied force, attributed to the varying contact area and number of contact points among the fibers. Long‐term outdoor exposure tests confirm the material's resilience, maintaining electrical conductivity and structural integrity. The study also demonstrates the sensor's ability to monitor human activities like walking, running, stair climbing, and jumping, as well as detecting muscle movements during swallowing, coughing, and speech, highlighting potential applications in health monitoring and artificial voice synthesis. The MRMR algorithm is used for feature selection to distinguish activities, demonstrating the sensor's potential for activity recognition. Additionally, a piezoelectric sensor on the pressure sensors estimates harvested electric power to power wearable devices. This work contributes to the advancement of self‐powered wearable pressure sensors to monitor real‐time human activity, with implications for healthcare, sports performance, and assistive technologies.

Nanomaterial-Enhanced Microneedles: Emerging Therapies for Diabetes and Obesity.

Drug delivery systems (DDS) have improved therapeutic agent administration by enhancing efficacy and patient compliance while minimizing side effects. They enable targeted delivery, controlled release, and improved bioavailability. Transdermal drug delivery systems (TDDS) offer non-invasive medication administration and have evolved to include methods such as chemical enhancers, iontophoresis, microneedles (MN), and nanocarriers. MN technology provides innovative solutions for chronic metabolic diseases like diabetes and obesity using various MN types. For diabetes management, MNs enable continuous glucose monitoring, diabetic wound healing, and painless insulin delivery. For obesity treatment, MNs provide sustained transdermal delivery of anti-obesity drugs or nanoparticles (NPs). Hybrid systems integrating wearable sensors and smart materials enhance treatment effectiveness and patient management. Nanotechnology has advanced drug delivery by integrating nano-scaled materials like liposomes and polymeric NPs with MNs. In diabetes management, glucose-responsive NPs facilitate smart insulin delivery. At the same time, lipid nanocarriers in dissolving MNs enable extended release for obesity treatment, enhancing drug stability and absorption for improved metabolic disorder therapies. DDS for obesity and diabetes are advancing toward personalized treatments using smart MN enhanced with nanomaterials. These innovative approaches can enhance patient outcomes through precise drug administration and real-time monitoring. However, widespread implementation faces challenges in ensuring biocompatibility, improving technologies, scaling production, and obtaining regulatory approval. This review will present recent advances in developing and applying nanomaterial-enhanced MNs for diabetes and obesity management while also discussing the challenges, limitations, and future perspectives of these innovative DDS.

Donor–π bridge-acceptor dyes featuring dual chromophoric groups for enhanced sensitivity in wearable sweat sensor

The pH value of human sweat serves as a crucial biomarker for disease diagnosis, rendering wearable sweat sensors an innovative tool for monitoring human health. However, the commercialization of these devices is seriously impeded by their low sensitivity, poor wearing comfort, and limited reusability. In this work, a reactive dye capable of color change under pH change has been synthesized. Due to the unique molecular structure, the dye molecule exhibits remarkable color change recognition and high sensitivity. To fabricate a wearable pH sensor, screen printing technology was employed for depositing the color-changing dye onto cotton fabric. The resulting sensor demonstrated excellent performance attributes including high color sensitivity, robust cyclic stability in response to pH changes, and exceptional resistance against washing, friction, and sunlight-induced fading. Therefore, this study presents a highly promising approach for achieving real-time monitoring of human sweat pH levels.

Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India).

The COVID-19 pandemic has challenged the current paradigm of clinical and community-based disease detection. We present a multimodal wearable sensor system paired with a two-minute, movement-based activity sequence that successfully captures a snapshot of physiological data (including cardiac, respiratory, temperature, and percent oxygen saturation). We conducted a large, multi-site trial of this technology across India from June 2021 to April 2022 amidst the COVID-19 pandemic (Clinical trial registry name: International Validation of Wearable Sensor to Monitor COVID-19 Like Signs and Symptoms; NCT05334680; initial release: 04/15/2022). An Extreme Gradient Boosting algorithm was trained to discriminate between COVID-19 infected individuals (n = 295) and COVID-19 negative healthy controls (n = 172) and achieved an F1-Score of 0.80 (95% CI = [0.79, 0.81]). SHAP values were mapped to visualize feature importance and directionality, yielding engineered features from core temperature, cough, and lung sounds as highly important. The results demonstrated potential for data-driven wearable sensor technology for remote preliminary screening, highlighting a fundamental pivot from continuous to snapshot monitoring of cardiorespiratory illnesses.

Minimum data sampling requirements for accurate detection of terrain-induced gait alterations change with mobile sensor position

Human gait is a key biomarker for health, independence and quality of life. Advances in wearable inertial sensor technologies have paved the way for out-of-the-lab human gait analysis, which is important for the assessment of mobility and balance in natural environments and has applications in multiple fields from healthcare to urban planning. Automatic recognition of the environment where walking takes place is a prerequisite for successful characterisation of terrain-induced gait alterations. A key question which remains unexplored in the field is how minimum data requirements for high terrain classification accuracy change depending on the sensor placement on the body. To address this question, we evaluate the changes in performance of five canonical machine learning classifiers by varying several data sampling parameters including sampling rate, segment length, and sensor configuration. Our analysis on two independent datasets clearly demonstrate that a single inertial measurement unit is sufficient to recognise terrain-induced gait alterations, accuracy and minimum data requirements vary with the device position on the body, and choosing correct data sampling parameters for each position can improve classification accuracy up to 40% or reduce data size by 16 times. Our findings highlight the need for adaptive data collection and processing algorithms for resource-efficient computing on mobile devices.

E-waste driven eco-friendly highly sensitive wearable capacitive sensors for physical activity monitoring and sign language recognition

Owing to the continual and rapid advancement in technology, there has been a significant surge in electronic waste (e-waste) in recent decades, resulting in a harmful impact on our environment. Due to the growing demand for battery-powered devices, non-rechargeable batteries are a significant source of e-waste. This study describes fabricating a paper-based graphite strain sensor that is eco-friendly, inexpensive, biodegradable, and sensitive to the applied strain. Readily accessible materials are employed to design and fabricate MIDC strain sensors. The Modified Interdigital Capacitor (MIDC) structure is formed on basic printing paper, and the conductive substance made with the ink of a gel pen and graphite from a drained dry cell is painted on the paper and used as electrode material. The suggested sensor has a high gauge factor value of 1998.578 and fast response and recovery time of 112 ms and 100 ms, respectively. The proposed prototype has also been utilized to inspect fitness exercises and physical activities on the human body, inspiring the potential for its integration into wearable systems for various applications. Reliable detection and differentiation of finger positions are achieved, highlighting the sensor's potential for more advanced sign language applications. It’s a good option to integrate the suggested strain sensor in wearable systems, such as telemedicine systems, electromechanical detection, healthcare, and physical activity monitoring of humans.

Highly Stretchable Sensitive Multiscale Hydrogel Inspired by Biological Muscles for Wearing Sensors.

Hydrogels have attracted substantial research interest for application in wearable electronics due to their stretchability, elasticity, and compliance. However, most hydrogels could not satisfy the application requirements for high-performance wearable sensors due to their poor sensitivity, low mechanical properties, and sensing detection range until this day. Inspired by the fascia in biological muscles, we propose a strategy to form entangled "clusters" through the dense entanglement between highly cross-linked elastic hydrogel microspheres and polymer segments, and prepared a multiscale hydrogel with high sensitivity and mechanical toughness. This strategy embedded highly swollen hydrogel microspheres (with different pore sizes) to act as the microregions of dense entanglement in the soft matrix to adjust the microstructure of multiscale gel. When pressure was applied, this structure could provide a fast response due to the stack layer formed by microspheres and soft matrix produced effective stress distribution, resulting in the outstanding sensitivity of the multiscale hydrogel (S = 1.1 kPa-1) in the pressure range of 0-50 kPa. The distinct microspheres functioning as microscale joint areas significantly augment energy dissipation, culminating in exceptional mechanical stability, ultrastretchability (≈1050%), and high strength of the multiscale hydrogel. The most notable progress was that the synthesized multiscale hydrogel not only combined the above advantages but also simultaneously solved multiple dilemmas of tedious synthesis steps, high cost, and poor durability. Besides, the multiscale hydrogel also had excellent antibacterial properties and biocompatibility, which enabled them to have large-scale application potential in wearable and implantable electronic devices. Our research could provide a universal approach to the creation of robust, flexible, wearable, and sensitive sensors, significantly increasing the uses of stress sensors in wearable technology.

Predicting Athlete Workload in Women's Rugby Sevens Using GNSS Sensor Data, Contact Count and Mass.

The use of session rating of perceived exertion (sRPE) as a measure of workload is a popular athlete load monitoring tool. However, the nature of sRPE means the contribution of salient, sport-specific factors to athlete load in field sports is challenging to isolate and quantify. In rugby sevens, drivers of load include high-speed running and physical contact. In soccer and men's rugby, union acceleration/deceleration also influences load. These metrics are evaluated using data from global navigation satellite system (GNSS) sensors worn by athletes. Research suggests that sensor data methods for identifying load in men's rugby do not accurately quantify female athlete loads. This investigation examined how mass, contact, and accelerations and decelerations at different speeds contribute to load in women's rugby sevens. The study evaluated 99 international matches, using data from 19 full-time athletes. GNSS measures, RPE, athlete mass, and contact count were evaluated using a linear mixed-model regression. The model demonstrated significant effects for low decelerations at low and high speeds, mass, distance, and contact count explaining 48.7% of the global variance of sRPE. The use of acceleration/deceleration and speed from GNSS sensors alongside mass, as well as contact count, presents a novel approach to quantifying load.

Carbon Nanotubes and Their Composites for Flexible Electrochemical Biosensors

AbstractFlexible biosensors play a crucial role for healthcare management and disease diagnosis. Electrochemical biosensors have attracted significant attention for wearable sensing applications owing to their numerous advantages, including high sensitivity and selectivity, inherent miniaturization and rapid response times. Challenges lie in the development of highly conductive and flexible electrodes that can be integrated with biorecognition components to engineer selective biosensor interfaces. Carbon nanotubes (CNTs) hold significant promise as materials for wearable flexible sensor fabrication. This review highlights recent strategies for fabricating conductive and flexible electrodes, whether in the form of films or fibers, based on CNTs and their composites. Additionally, the review explores emerging biosensing applications, including flexible sensors for the direct electrochemical detection of biomarkers, sensors functionalized with enzymes, antibodies, or DNA, and sensors interfaced with cells to monitor transient biochemical signals.

Sustainable Engineering of the Wearable Sensor for Noninvasive Health Monitoring Using Exfoliated Layered Double Hydroxide/Reduced Graphene Oxide/Poly(vinyl alcohol) Electrospun Fiber Composites

Sustainable Engineering of the Wearable Sensor for Noninvasive Health Monitoring Using Exfoliated Layered Double Hydroxide/Reduced Graphene Oxide/Poly(vinyl alcohol) Electrospun Fiber Composites

Rhythm-Ready: Harnessing Smart Devices to Detect and Manage Arrhythmias.

To survey recent progress in the application of implantable and wearable sensors to detection and management of cardiac arrhythmias. Sensor-enabled strategies are critical for the detection, prediction and management of arrhythmias. In the last several years, great innovation has occurred in the types of devices (implanted and wearable) that are available and the data they collect. The integration of artificial intelligence solutions into sensor-enabled strategies has set the stage for a new generation of smart devices that augment the human clinician. Smart devices enhanced by new sensor technologies and Artificial Intelligence (AI) algorithms promise to reshape the care of cardiac arrhythmias.

Robust conductive hydrogel advances self-powered intelligent sports monitoring and fair judging

Currently, combining multimodal perceptual ability with mechanical robustness poses a significant challenge for developing hydrogel-based flexible electronics. Herein, a robust conductive hydrogel was successfully synthesized by introducing cellulose nanocrystals (CNC) as nanofillers and lithium chloride (LiCl) as the conductive component into the copolymer network through a one-pot crosslinking procedure in a water-dimethyl sulfoxide binary solvent. Besides excellent stretchable, self-adhesive, anti-freezing, self-healing, and anti-swelling properties, tunable and reversible optical property was realized due to the polarity-induced microphase separation of the hydrophilic and hydrophobic group in the zwitterionic sulfobetaine chains, enabling cyclic information encryption and decryption. An all-weather wearable sensor array was thus developed for multi-modal sensing of various environmental stimuli, including mechanical, and thermal variations. Specifically, the intensity and spatial distribution of strain can be detected almost in real time within a broad strain range, thus realizing speech recognation, text-to-speech and human electrocardiogram signal acquisition and underwater non-contact sensing functions. Notably, the CPAMD-based triboelectric nanogenerator demonstrates impressive temperature tolerance (−40 °C and 60 °C), and remarkable material identification capability. A non-contact proximity sensing sensor and a foul detection system were thus developed by coupling the piezoresistive, triboelectric and capacitive sensing metrics to serve as an electronic referee for identifying foul in curling. This work provides a valuable reference toward combining excellent mechanical robustness with balanced electrical performance for tactile perception and intelligent sports monitoring.

Targeting Sodium in Heart Failure.

A dominant event determining the course of heart failure (HF) includes the disruption of the delicate sodium (Na+) and water balance leading to (Na+) and water retention and edema formation. Although incomplete decongestion adversely affects outcomes, it is unknown whether interventions directly targeting (Na+), such as strict dietary (Na+) restriction, intravenous hypertonic saline, and diuretics, reverse this effect. As a result, it is imperative to implement (Na+)-targeting interventions in selected HF patients with established congestion on top of quadruple therapy with angiotensin receptor neprilysin inhibitor, β-adrenergic receptor blocker, mineralocorticoid receptor antagonist, and sodium glucose cotransporter 2 inhibitor, which dramatically improves outcomes. The limited effectiveness of (Na+)-targeting treatments may be partly due to the fact that the current metrics of HF severity have a limited capacity of foreseeing and averting episodes of congestion and guiding (Na+)-targeting treatments, which often leads to dysnatremias, adversely affecting outcomes. Recent evidence suggests that spot urinary sodium measurements may be used as a guide to monitor (Na+)-targeting interventions both in chronic and acute HF. Further, the classical (2)-compartment model of (Na+) storage has been displaced by the (3)-compartment model emphasizing the non-osmotic accumulation of (Na+), chiefly in the skin. 23(Na+) magnetic resonance imaging (MRI) enables the accurate and reliable quantification of tissue (Na+). Another promising approach enabling tissue (Na+) monitoring is based on wearable devices employing ion-selective electrodes for electrolyte detection, including (Na+) and (Cl-). Undoubtably, further studies using 23(Na+)-MRI technology and wearable sensors are required to learn more about the clinical significance of tissue (Na+) storage and (Na+)-related mechanisms of morbidity and mortality in HF.

The Rise of Biochemical Sensors: Technology for Real-Time Health Monitoring (Applications and Future Scope)

Biochemical sensors integrated into wearable health technology represent one of the most transformative innovations of the 21st century. This review delves into the evolution, principles, and real-time applications of biochemical sensors and their role in personalized health monitoring. Wearable biochemical sensors, capable of continuously measuring various biomarkers like glucose, lactate, cortisol, and electrolytes, are revolutionizing healthcare by enabling proactive management of chronic diseases, including diabetes, cardiovascular disorders, and mental health issues. Advances in biosensing technologies, coupled with the use of AI and machine learning algorithms, have enhanced the sensitivity and accuracy of these devices, ensuring that critical health data is available in real-time. From glucose monitoring devices like Abbott's FreeStyle Libre to the latest nanomaterial-based sensors, these innovations are reshaping healthcare delivery by shifting the focus from hospital-centered treatments to patient-centric, continuous monitoring systems. This review provides an in-depth analysis of the technological advancements, challenges, and future directions in biochemical sensors, focusing on key technologies such as electrochemical, optical, and enzymatic sensors. It also highlights the critical role of AI in interpreting the complex data generated by these sensors, paving the way for more efficient diagnostics and predictive healthcare models. Furthermore, the paper explores how these sensors have been applied in infectious disease detection, particularly during the COVID-19 pandemic, and discusses their potential to enhance global health surveillance systems. In conclusion, wearable biochemical sensors represent a significant leap forward in the pursuit of personalized medicine, offering real-time diagnostics and timely interventions for disease management. The future of healthcare is closely tied to the ongoing innovations in sensor technology, with the promise of even more advanced and multifunctional devices on the horizon.

Time–frequency domain machine learning for detection of epilepsy using wearable EEG sensor signals recorded during physical activities

Epilepsy is a neurological ailment in which there is a disturbance in the nerve cell activity of the brain, causing recurrent seizures. The electroencephalogram (EEG) signal is widely used as a diagnostic modality to detect epilepsy ailment. The automated detection of epilepsy using wearable EEG sensor data recorded during various physical activities is interesting for continuous monitoring of brain health. This paper proposes a time–frequency (TF) domain machine learning (ML) approach for the automated detection of epilepsy using wearable sensor-based EEG signals. The Gaussian window-based Stockwell transform (GWST) is employed to evaluate the TF matrix from the EEG signal. The features such as the L1-norm and the Shannon entropy are extracted from the TF matrix of the EEG signal. The ML and deep learning (DL) models are employed to detect epilepsy using the TF domain features of EEG signals. The publicly available database containing wearable sensor-based EEG signals recorded from the subjects while performing different physical activities is used to evaluate the performance of the proposed approach. The results show that the random forest (RF) classifier coupled with GWST domain features of EEG signals has obtained an overall accuracy value of 90.74% for detecting epilepsy with hold-out validation using the EEG signals from different physical activity cases. For the 10-fold cross-validation (CV) case, the GWST domain features of EEG signal and multi-layer long short-term memory (LSTM) classifier have produced the average accuracy value of 74.44%. For jogging, running, and idle sitting activities, the GWST-based TF domain entropy features coupled with the multi-layer LSTM model have obtained accuracy values of 82.72%, 82.41%, and 87.30%, respectively. The proposed approach has achieved higher classification accuracy than existing methods to detect epilepsy using wearable sensor-based EEG signals using a 10-fold CV strategy. The suggested approach is compared with existing methods to classify seizure and seizure-free classes using resting-state EEG signals.

Developing biomimetic PVA/PAA hydrogels with cellulose nanocrystals inspired by tree frog structures for superior wearable sensor functionality

This study developed a wearable sensor device featuring biomimetic structures inspired by tree frog toe pads to measure shoulder joint movements effectively. A PVA/PAA double-network hydrogel was formulated by mixing PVA and PAA in a 3:7 ratio, resulting in a Young's modulus of 7.2 kPa, closely matching human skin's mechanical properties. To enhance moisture retention, the hydrogel was supplemented with 20 mL glycerin, increasing its weight retention rate to 95 % after 28 days, a 5.6-fold improvement over glycerin-free hydrogels. Additionally, the incorporation of 0.2 wt% cellulose nanocrystals (CNC) increased the cross-linking density, reducing the swelling ratio from 124 % to 106 % and minimizing the impact of swelling cycles on mechanical properties from 22 % to 4 %. Treefrog-inspired microstructures were fabricated on the sensor surface using laser cutting and casting techniques, significantly enhancing adhesion under humid conditions, with normal, lateral, and peel-off adhesion forces improving by 500, 700, and 700 %, respectively. The sensor demonstrated accurate measurement of shoulder joint movements with an error margin of 6.3 % for polar angles (45°-135°) and 7.8 % for azimuth angles (0°-45°). The accompanying smartphone application provided real-time feedback, helping to prevent exercise injuries and improve user awareness and control of shoulder movements, making it suitable for both rehabilitation and athletic training.

Convolution neural network based multi-class classification of rehabilitation exercises for diastasis recti abdominis using wearable EMG-IMU sensors

PurposeGlobally, postnatal women endure a prominent issue caused by midline separation of abdominal recti muscles, characterized by a sagging and pouch-like appearance of the belly termed as Diastasis Recti Abdominis (DRA). The necessity of ensuring the efficacy of rehabilitative workouts for individuals with DRA cannot be overstated, as inaccurate exercises can exacerbate the condition and deteriorate the health of affected women. The purpose of these exercises is to specifically focus on the rectus abdominis muscles to facilitate the reapproximation of the linea alba. The primary aim of this research work is to assess the effectiveness of rehabilitation exercises for DRA women obtained from Inertial Measurement Unit (IMU) and Electromyography (EMG) sensors.Design/methodology/approachConvolutional neural networks (CNN) employs convolutional activation functions and pooling layers. Recently, 1D CNNs have emerged as a promising approach used in various applications, including personalized biomedical data classification and early diagnosis, structural health monitoring and anomaly detection. Yet another significant benefit is the feasibility of a real-time and cost-effective implementation of 1D CNN. The EMG and IMU signals serve as inputs for the 1D CNN. Features are then extracted from the fully connected layer of the CNN and fed into a boosting machine learning algorithm for classification.FindingsThe findings demonstrate that a combination of sensors provides more details about the exercises, thereby contributing to the classification accuracy.Practical implicationsIn real time, collecting data from postnatal women was incredibly challenging. The process of examining these women was time-consuming, and they were often preoccupied with their newborns, leading to a reluctance to focus on their own health. Additionally, postnatal women might not be fully aware of the implications of DRA and the importance of rehabilitation exercises. Many might not realize that neglecting DRA can lead to long-term issues such as back pain, pelvic floor dysfunction, and compromised core strength.Social implicationsDuring our data collection camps, there were educational sessions to raise awareness about the DRA problem and the benefits of rehabilitation exercises. This dual approach helped in building trust and encouraging participation. Moreover, the use of wearable sensors in this study provided a non-invasive and convenient way for new mothers to engage in rehabilitation exercises without needing frequent visits to a clinic, which is often impractical for them.Originality/valueThe utilization of discriminating features retrieved from the output layer of 1D CNN is a significant contribution to this work. The responses of this study indicate that 1D convolutional neural network (1D CNN) and Boosting algorithms used in a transfer learning strategy produce successful discrimination between accurate and inaccurate performance of exercises by achieving an accuracy of 96%.