Shoe-Integrated, Force Sensor Design for Continuous Body Weight Monitoring

Validation of Parental Reports in Assessing Idiopathic Toe Walking Using Quantitative In-Shoe Device Measurements

BACKGROUND

Toe walking is prevalent among children, affecting 5% to 24% of the pediatric population. Clinicians rely on parental reports of frequency of toe walking to guide clinical decision making and outcomes assessment. However, recall accuracy and differing environments challenge the reliability of parental reports. This study aims to validate parental reports against quantitative in-shoe device measurements (NURVV/RUN).

METHODS

Twenty children with persistent idiopathic toe walking (ITWp) (mean age: 9.6y; 13 males, 7 females) from 8 pediatric orthopaedic specialty care sites participated in this multicenter study. Parents assessed toe walking frequency using a 6-point scale, while children wore NURVV/RUN insoles for 8 hours/day over 7 days. Insole sensors recorded foot strike patterns (rearfoot, midfoot, and forefoot), which were classified using the same severity scale. Agreement between parental reports and NURVV data was assessed using weighted Kappa statistics (P<0.05).

RESULTS

Before intervention, children with ITWp exhibited daily walking patterns: 61.7% forefoot, 15.3% midfoot, and 22.8% hindfoot contact. Agreement analysis showed substantial agreement (k=0.688, P<0.001) for combined forefoot and midfoot contacts and fair agreement (k=0.381, P<0.005) for isolated forefoot contact.

CONCLUSION

Parental reports of toe walking prevalence in their children are relatively accurate, supporting their use in clinical management. However, quantitative in-shoe devices provide a more objective and quantitative understanding of ITWp frequency and have the potential to guide clinical decision-making and outcomes assessment children with ITWp.

LEVEL OF EVIDENCE

Level II-diagnostic study. See instructions to authors for a complete description of levels of evidence.

Noninvasive biometric monitoring technologies for patients with heart failure

Heart failure remains one of the leading causes of mortality and hospitalizations in the US that not only impacts quality of life but also poses a significant public health burden. The majority of affected patients are admitted with signs and symptoms of congestion. Despite the initial enthusiasm, traditional remote monitoring strategies focusing primarily on weight gain failed to improve clinical outcomes. Implantable pulmonary artery pressure sensors provide earlier and actionable data, but most patients would favor forgoing an invasive procedure in favor of an alternative, non-invasive monitoring platform. Several devices utilizing different combinations of multiparameter monitoring to reliably detect congestion have recently been developed and are undergoing testing in the clinical setting. Combining these sensors with the power of artificial intelligence and machine learning has the potential to revolutionize remote patient monitoring and early congestion detection and to facilitate timely interventions by the care team to prevent hospitalization. This manuscript provides an objective review of novel, noninvasive, multiparameter remote monitoring platforms that may be tailored to individual heart failure phenotypes, aiming to improve quality of life and survival.

Adaptive Lifting Index (aLI) for Real-Time Instrumental Biomechanical Risk Assessment: Concepts, Mathematics, and First Experimental Results

When performing lifting tasks at work, the Lifting Index (LI) is widely used to prevent work-related low-back disorders, but it presents criticalities pertaining to measurement accuracy and precision. Wearable sensor networks, such as sensorized insoles and inertial measurement units, could improve biomechanical risk assessment by enabling the computation of an adaptive LI (aLI) that changes over time in relation to the actual method of carrying out lifting. This study aims to illustrate the concepts and mathematics underlying aLI computation and compare aLI calculations in real-time using wearable sensors and force platforms with the LI estimated with the standard method used by ergonomists and occupational health and safety technicians. To reach this aim, 10 participants performed six lifting tasks under two risk conditions. The results show us that the aLI value rapidly converges towards the reference value in all tasks, suggesting a promising use of adaptive algorithms and instrumental tools for biomechanical risk assessment.

The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review

Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.

An Individualized Machine Learning Approach for Human Body Weight Estimation Using Smart Shoe Insoles

Rapid significant weight fluctuations can indicate severe health conditions such as edema due to congestive heart failure or severe dehydration that could require prompt intervention. Daily body weighing does not accurately represent the patient's body weight fluctuations occurring within a day. The patient's lack of compliance with tracking their weight measurements is also a predominant issue. Using shoe insole sensors embedded into footwear could achieve accurate real-time monitoring systems for estimating continuous body weight changes. Here, the machine learning models' predictive capabilities for continuous real-time weight estimation using the insole data are presented. The lack of availability of public datasets to feed these models is also addressed by introducing two novel datasets. The proposed framework is designed to adapt to the patient, considering several unique factors such as shoe type, posture, foot shape, and gait pattern. The proposed framework estimates the mean absolute percentage error of 0.61% and 0.74% and the MAE of 1.009 lbs. and 1.154 lbs. for the less controlled and more controlled experimental settings, respectively. This will help researchers utilize machine learning techniques for more accurate real-time continuous weight estimation using sensor data and enable more reliable aging-in-place monitoring and telehealth.

Ergonomic human-robot collaboration in industry: A review

In the current industrial context, the importance of assessing and improving workers' health conditions is widely recognised. Both physical and psycho-social factors contribute to jeopardising the underlying comfort and well-being, boosting the occurrence of diseases and injuries, and affecting their quality of life. Human-robot interaction and collaboration frameworks stand out among the possible solutions to prevent and mitigate workplace risk factors. The increasingly advanced control strategies and planning schemes featured by collaborative robots have the potential to foster fruitful and efficient coordination during the execution of hybrid tasks, by meeting their human counterparts' needs and limits. To this end, a thorough and comprehensive evaluation of an individual's ergonomics, i.e. direct effect of workload on the human psycho-physical state, must be taken into account. In this review article, we provide an overview of the existing ergonomics assessment tools as well as the available monitoring technologies to drive and adapt a collaborative robot's behaviour. Preliminary attempts of ergonomic human-robot collaboration frameworks are presented next, discussing state-of-the-art limitations and challenges. Future trends and promising themes are finally highlighted, aiming to promote safety, health, and equality in worldwide workplaces.

Flexible Ultra-Thin Nanocomposite Based Piezoresistive Pressure Sensors for Foot Pressure Distribution Measurement

Foot pressure measurement plays an essential role in healthcare applications, clinical rehabilitation, sports training and pedestrian navigation. Among various foot pressure measurement techniques, in-shoe sensors are flexible and can measure the pressure distribution accurately. In this paper, we describe the design and characterization of flexible and low-cost multi-walled carbon nanotubes (MWCNT)/Polydimethylsiloxane (PDMS) based pressure sensors for foot pressure monitoring. The sensors have excellent electrical and mechanical properties an show a stable response at constant pressure loadings for over 5000 cycles. They have a high sensitivity of 4.4 kΩ/kPa and the hysteresis effect corresponds to an energy loss of less than 1.7%. The measurement deviation is of maximally 0.13% relative to the maximal relative resistance. The sensors have a measurement range of up to 330 kPa. The experimental investigations show that the sensors have repeatable responses at different pressure loading rates (5 N/s to 50 N/s). In this paper, we focus on the demonstration of the functionality of an in-sole based on MWCNT/PDMS nanocomposite pressure sensors, weighing approx. 9.46 g, by investigating the foot pressure distribution while walking and standing. The foot pressure distribution was investigated by measuring the resistance changes of the pressure sensors for a person while walking and standing. The results show that pressure distribution is higher in the forefoot and the heel while standing in a normal position. The foot pressure distribution is transferred from the heel to the entire foot and further transferred to the forefoot during the first instance of the gait cycle.