Triode-Mimicking Graphene Pressure Sensor with Positive Resistance Variation for Physiology and Motion Monitoring

The flexible pressure sensor is one of the essential components of the wearable device, which is a critical solution to the applications of artificial intelligence and human-computer interactions in the future. Due to its simple manufacturing process and measurement methods, research related to piezoresistive mechanical sensors is booming, and those sensors are already widely used in industry. However, existing pressure sensors are almost all based on negative resistance variations, making it difficult to reach a balance between the sensitivity and the detection range. Here, we demonstrated a low-cost flexible pressure sensor with a positive resistance-pressure response based on laser scribing graphene. The sensor can be customized and modulated to achieve both an ultrahigh sensitivity and a broad detection range. Furthermore, the device possesses the signal amplification property like a mechanical triode under the external pressure bias. Based on its amplification ability, varieties of physiological signals and human movements have been detected using our devices; then, an integrated gait monitoring system has been realized. The reported positive graphene pressure sensor has outstanding capability, showing a wide application range such as intelligent perception, an interactive device, and real-time health/motion monitoring.

An Ambulatory System for Gait Monitoring Based on Wireless Sensorized Insoles

A new gait phase detection system for continuous monitoring based on wireless sensorized insoles is presented. The system can be used in gait analysis mobile applications, and it is designed for real-time demarcation of gait phases. The system employs pressure sensors to assess the force exerted by each foot during walking. A fuzzy rule-based inference algorithm is implemented on a smartphone and used to detect each of the gait phases based on the sensor signals. Additionally, to provide a solution that is insensitive to perturbations caused by non-walking activities, a probabilistic classifier is employed to discriminate walking forward from other low-level activities, such as turning, walking backwards, lateral walking, etc. The combination of these two algorithms constitutes the first approach towards a continuous gait assessment system, by means of the avoidance of non-walking influences.

Stride Counting in Human Walking and Walking Distance Estimation Using Insole Sensors

This paper proposes a novel method of estimating walking distance based on a precise counting of walking strides using insole sensors. We use an inertial triaxial accelerometer and eight pressure sensors installed in the insole of a shoe to record walkers' movement data. The data is then transmitted to a smartphone to filter out noise and determine stance and swing phases. Based on phase information, we count the number of strides traveled and estimate the movement distance. To evaluate the accuracy of the proposed method, we created two walking databases on seven healthy participants and tested the proposed method. The first database, which is called the short distance database, consists of collected data from all seven healthy subjects walking on a 16 m distance. The second one, named the long distance database, is constructed from walking data of three healthy subjects who have participated in the short database for an 89 m distance. The experimental results show that the proposed method performs walking distance estimation accurately with the mean error rates of 4.8% and 3.1% for the short and long distance databases, respectively. Moreover, the maximum difference of the swing phase determination with respect to time is 0.08 s and 0.06 s for starting and stopping points of swing phases, respectively. Therefore, the stride counting method provides a highly precise result when subjects walk.

Neural Correlates of Single- and Dual-Task Walking in the Real World

Recent developments in mobile brain-body imaging (MoBI) technologies have enabled studies of human locomotion where subjects are able to move freely in more ecologically valid scenarios. In this study, MoBI was employed to describe the behavioral and neurophysiological aspects of three different commonly occurring walking conditions in healthy adults. The experimental conditions were self-paced walking, walking while conversing with a friend and lastly walking while texting with a smartphone. We hypothesized that gait performance would decrease with increased cognitive demands and that condition-specific neural activation would involve condition-specific brain areas. Gait kinematics and high density electroencephalography (EEG) were recorded whilst walking around a university campus. Conditions with dual tasks were accompanied by decreased gait performance. Walking while conversing was associated with an increase of theta (θ) and beta (β) neural power in electrodes located over left-frontal and right parietal regions, whereas walking while texting was associated with a decrease of β neural power in a cluster of electrodes over the frontal-premotor and sensorimotor cortices when compared to walking whilst conversing. In conclusion, the behavioral “signatures” of common real-life activities performed outside the laboratory environment were accompanied by differing frequency-specific neural “biomarkers”. The current findings encourage the study of the neural biomarkers of disrupted gait control in neurologically impaired patients.

Estimating Lower Extremity Running Gait Kinematics with a Single Accelerometer: A Deep Learning Approach

Abnormal running kinematics are associated with an increased incidence of lower extremity injuries among runners. Accurate and unobtrusive running kinematic measurement plays an important role in the detection of gait abnormalities and the prevention of injuries among runners. Inertial-based methods have been proposed to address this need. However, previous methods require cumbersome sensor setup or participant-specific calibration. This study aims to validate a shoe-mounted accelerometer for sagittal plane lower extremity angle measurement during running based on a deep learning approach. A convolutional neural network (CNN) architecture was selected as the regression model to generalize in inter-participant scenarios and to minimize poorly estimated joints. Motion and accelerometer data were recorded from ten participants while running on a treadmill at five different speeds. The reference joint angles were measured by an optical motion capture system. The CNN model predictions deviated from the reference angles with a root mean squared error (RMSE) of less than 3.5° and 6.5° in intra- and inter-participant scenarios, respectively. Moreover, we provide an estimation of six important gait events with a mean absolute error of less than 2.5° and 6.5° in intra- and inter-participants scenarios, respectively. This study highlights an appealing minimal sensor setup approach for gait analysis purposes.

Ultralightweight and 3D Squeezable Graphene-Polydimethylsiloxane Composite Foams as Piezoresistive Sensors

The growing demand for flexible, ultrasensitive, squeezable, skin-mountable, and wearable sensors tailored to the requirements of personalized health-care monitoring has fueled the necessity to explore novel nanomaterial-polymer composite-based sensors. Herein, we report a sensitive, 3D squeezable graphene-polydimethylsiloxane (PDMS) foam-based piezoresistive sensor realized by infusing multilayered graphene nanoparticles into a sugar-scaffolded porous PDMS foam structure. Static and dynamic compressive strain testing of the resulting piezoresistive foam sensors revealed two linear response regions with an average gauge factor of 2.87-8.77 over a strain range of 0-50%. Furthermore, the dynamic stimulus-response revealed the ability of the sensors to effectively track dynamic pressure up to a frequency of 70 Hz. In addition, the sensors displayed a high stability over 36000 cycles of cyclic compressive loading and 100 cycles of complete human gait motion. The 3D sensing foams were applied to experimentally demonstrate accurate human gait monitoring through both simulated gait models and real-time gait characterization experiments. The real-time gait experiments conducted demonstrate that the information of the pressure profile obtained at three locations in the shoe sole could not only differentiate between different kinds of human gaits including walking and running but also identify possible fall conditions. This work also demonstrates the capability of the sensors to differentiate between foot anatomies, such as a flat foot (low central arch) and a medium arch foot, which is biomechanically more efficient. Furthermore, the sensors were able to sense various basic joint movement responses demonstrating their suitability for personalized health-care applications.

Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson's Disease

Freezing of Gait (FoG) is a common symptom in Parkinson's Disease (PD) occurring with significant variability and severity and is associated with increased risk of falls. FoG detection in everyday life is not trivial, particularly in patients manifesting the symptom only in specific conditions. Various wearable devices have been proposed to detect PD symptoms, primarily based on inertial sensors. We here report the results of the validation of a novel system based on a pair of pressure insoles equipped with a 3D accelerometer to detect FoG episodes. Twenty PD patients attended a motor assessment protocol organized into eight multiple video recorded sessions, both in clinical and ecological settings and both in the ON and OFF state. We compared the FoG episodes detected using the processed data gathered from the insoles with those tagged by a clinician on video recordings. The algorithm correctly detected 90% of the episodes. The false positive rate was 6% and the false negative rate 4%. The algorithm reliably detects freezing of gait in clinical settings while performing ecological tasks. This result is promising for freezing of gait detection in everyday life via wearable instrumented insoles that can be integrated into a more complex system for comprehensive motor symptom monitoring in PD.

Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications

The present paper reviews, for the first time, to the best of our knowledge, the most recent advances in research concerning two popular devices used for foot motion analysis and health monitoring: smart socks and in-shoe systems. The first one is representative of textile-based systems, whereas the second one is one of the most used pressure sensitive insole (PSI) systems that is used as an alternative to smart socks. The proposed methods are reviewed for smart sock use in special medical applications, for gait and foot pressure analysis. The Pedar system is also shown, together with studies of validation and repeatability for Pedar and other in-shoe systems. Then, the applications of Pedar are presented, mainly in medicine and sports. Our purpose was to offer the researchers in this field a useful means to overview and select relevant information. Moreover, our review can be a starting point for new, relevant research towards improving the design and functionality of the systems, as well as extending the research towards other areas of applications using sensors in smart textiles and in-shoe systems.

Fabrication of β-Phase-Enriched PVDF Sheets for Self-Powered Piezoelectric Sensing

The fabrication of self-powered pressure sensors based on piezoelectric materials requires flexible piezoelectric generators produced with a continuous, large-scale, and environmentally friendly approach. In this study, continuous poly(vinylidene fluoride) (PVDF) sheets with a higher β-phase content were facilely fabricated by the melt-extrusion-calendering technique and a PVDF-based piezoelectric generator (PEG) was further assembled. Such a PEG exhibits a remarkable piezoelectric output performance. Moreover, it possesses prominent stability even after working for a long time, exhibiting potential applications for real-time monitoring of various human movements (i.e., hopping, running, and walking) and gait. This work not only provides the possibility of continuous and environmentally friendly fabrication of PVDF sheets with remarkable piezoelectric properties but also paves a new promising pathway for powering portable microelectronic applications without any external power supply.

Sensor-Based Fall Risk Assessment: A Survey

Fall is a major problem leading to serious injuries in geriatric populations. Sensor-based fall risk assessment is one of the emerging technologies to identify people with high fall risk by sensors, so as to implement fall prevention measures. Research on this domain has recently made great progress, attracting the growing attention of researchers from medicine and engineering. However, there is a lack of studies on this topic which elaborate the state of the art. This paper presents a comprehensive survey to discuss the development and current status of various aspects of sensor-based fall risk assessment. Firstly, we present the principles of fall risk assessment. Secondly, we show knowledge of fall risk monitoring techniques, including wearable sensor based and non-wearable sensor based. After that we discuss features which are extracted from sensors in fall risk assessment. Then we review the major methods of fall risk modeling and assessment. We also discuss some challenges and promising directions in this field at last.

Self-Powered Smart Insole for Monitoring Human Gait Signals

With the rapid development of low-power consumption wireless sensors and wearable electronics, harvesting energy from human motion to enable self-powered sensing is becoming desirable. Herein, a pair of smart insoles integrated with piezoelectric poly(vinylidene fluoride) (PVDF) nanogenerators (NGs) are fabricated to simultaneously harvest energy from human motion and monitor human gait signals. Multi-target magnetron sputtering technology is applied to form the aluminum electrode layers on the surface of the PVDF film and the self-powered insoles are fabricated through advanced 3D seamless flat-bed knitting technology. Output responses of the NGs are measured at different motion speeds and a maximum value of 41 V is obtained, corresponding to an output power of 168.1 μW. By connecting one NG with an external circuit, the influence of external resistance, capacitor, and motion speed on the charging characteristics of the system is systematically investigated. To demonstrate the potential of the smart insoles for monitoring human gait signals, two subjects were asked to walk on a treadmill at different speeds or with a limp. The results show that one can clearly distinguish walking with a limp from regular slow, normal, and fast walking states by using multiscale entropy analysis of the stride intervals.

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

Traditional pedobarography methods use direct force sensor placement in the shoe insole to record pressure patterns. One problem with such methods is that they tap only a few points on the flat sole under the foot and, therefore, do not account for the total ground reaction force. As a result, body weight tends to be under-estimated. This disadvantage has made it more difficult for pedobarography to be used to monitor many diseases, especially when their symptoms include body weight changes. In this paper, the problem of pedobarographic body weight measurement is addressed using a novel ergonomic shoe-integrated sensor array architecture based on concentrating the applied force via three-layered structures that we call Sandwiched Sensor Force Consolidators (SSFC). A shoe prototype is designed with the proposed sensors and shown to accurately measure body weight with an achievable relative accuracy greater than 99%, even in the presence of motion. The achieved relative accuracy is at least 4X better than the existing state of the art. The SSFC shoe prototype is built using readily available soccer shoes and piezoresistive FlexiForce sensors. To improve the wearability and comfort of the instrumented shoe, a semi-computational sensor design methodology is developed based on an equivalent-area concept that can accurately account for SSFC's with arbitrary shapes. The search space of the optimal SSFC design is shown to be combinatorial, and a high-performance computing (HPC) framework based on OpenMP parallel programming is proposed to accelerate the design optimization process. An optimal sensor design speedup of up to 22X is shown to be achievable using the HPC implementation.

Adaptive Accumulation of Plantar Pressure for Ambulatory Activity Recognition and Pedestrian Identification

In this paper, we propose a novel method for ambulatory activity recognition and pedestrian identification based on temporally adaptive weighting accumulation-based features extracted from categorical plantar pressure. The method relies on three pressure-related features, which are calculated by accumulating the pressure of the standing foot in each step over three different temporal weighting forms. In addition, we consider a feature reflecting the pressure variation. These four features characterize the standing posture in a step by differently weighting step pressure data over time. We use these features to analyze the standing foot during walking and then recognize ambulatory activities and identify pedestrians based on multilayer multiclass support vector machine classifiers. Experimental results show that the proposed method achieves 97% accuracy for the two tasks when analyzing eight consecutive steps. For faster processing, the method reaches 89.9% and 91.3% accuracy for ambulatory activity recognition and pedestrian identification considering two consecutive steps, respectively, whereas the accuracy drops to 83.3% and 82.3% when considering one step for the respective tasks. Comparative results demonstrated the high performance of the proposed method regarding accuracy and temporal sensitivity.

Abnormal Gait Detection Using Wearable Hall-Effect Sensors

Abnormalities and irregularities in walking (gait) are predictors and indicators of both disease and injury. Gait has traditionally been monitored and analyzed in clinical settings using complex video (camera-based) systems, pressure mats, or a combination thereof. Wearable gait sensors offer the opportunity to collect data in natural settings and to complement data collected in clinical settings, thereby offering the potential to improve quality of care and diagnosis for those whose gait varies from healthy patterns of movement. This paper presents a gait monitoring system designed to be worn on the inner knee or upper thigh. It consists of low-power Hall-effect sensors positioned on one leg and a compact magnet positioned on the opposite leg. Wireless data collected from the sensor system were used to analyze stride width, stride width variability, cadence, and cadence variability for four different individuals engaged in normal gait, two types of abnormal gait, and two types of irregular gait. Using leg gap variability as a proxy for stride width variability, 81% of abnormal or irregular strides were accurately identified as different from normal stride. Cadence was surprisingly 100% accurate in identifying strides which strayed from normal, but variability in cadence provided no useful information. This highly sensitive, non-contact Hall-effect sensing method for gait monitoring offers the possibility for detecting visually imperceptible gait variability in natural settings. These nuanced changes in gait are valuable for predicting early stages of disease and also for indicating progress in recovering from injury.

Generation of Gait Events with a FSR Based Cane Handle

Gait analysis has many applications, and specifically can improve the control of prosthesis, exoskeletons, or Functional Electrical Stimulation systems. The use of canes is common to complement the assistance in these cases, and the synergy between upper and lower limbs can be exploited to obtain information about the gait. This is interesting especially in the case of unilateral assistance, for instance in the case of one side lower limb exoskeletons. If the cane is instrumented, it can hold sensors that otherwise should be attached to the body of the impaired user. This can ease the use of the assistive system in daily life as well as its acceptance. Moreover, Force Sensing Resistors (FSRs) are common in gait phase detection systems, and force sensors are also common in user intention detection. Therefore, a cane that incorporates FSRs on the handle can take advantage from the direct interface with the human and provide valuable information to implement real-time control. This is done in this paper, and the results confirm that many events are detected from variables derived from the readings of the FSRs that provide rich information about gait. However, a large inter-subject variability points to the need of tailored control systems.

Polymer Optical Fiber Plantar Pressure Sensors: Design and Validation

The proper measurement of plantar pressure during gait is critical for the clinical diagnosis of foot problems. Force platforms and wearable devices have been developed to study gait patterns during walking or running. However, these devices are often expensive, cumbersome, or have boundary constraints that limit the participant’s motions. Recent advancements in the quality of plastic optical fiber (POF) have made it possible to manufacture a low-cost bend sensor with a novel design for use in plantar pressure monitoring. An intensity-based POF bend sensor is not only lightweight, non-invasive, and easy to construct, but it also produces a signal that requires almost no processing. In this work, we have designed, fabricated, and characterized a novel intensity POF sensor to detect the force applied by the human foot and measure the gait pattern. The sensors were put through a series of dynamic and static tests to determine their measurement range, sensitivity, and linearity, and their response was compared to that of two different commercial force sensors, including piezo resistive sensors and a clinical force platform. The results suggest that this novel POF bend sensor can be used in a wide range of applications, given its low cost and non-invasive nature. Feedback walking monitoring for ulcer prevention or sports performance could be just one of those applications.

A Scoping Review of Pressure Measurements in Prosthetic Sockets of Transfemoral Amputees during Ambulation: Key Considerations for Sensor Design

Sensor systems to measure pressure at the stump-socket interface of transfemoral amputees are receiving increasing attention as they allow monitoring to evaluate patient comfort and socket fit. However, transfemoral amputees have many unique characteristics, and it is unclear whether existing research on sensor systems take these sufficiently into account or if it is conducted in ways likely to lead to substantial breakthroughs. This investigation addresses these concerns through a scoping review to profile research regarding sensors in transfemoral sockets with the aim of advancing and improving prosthetic socket design, comfort and fit for transfemoral amputees. Publications found from searching four scientific databases were screened, and 17 papers were found relating to the aim of this review. After quality assessment, 12 articles were finally selected for analysis. Three main contributions are provided: a de facto methodology for experimental studies on the implications of intra-socket pressure sensor use for transfemoral amputees; the suggestion that associated sensor design breakthroughs would be more likely if pressure sensors were developed in close combination with other types of sensors and in closer cooperation with those in possession of an in-depth domain knowledge in prosthetics; and that this research would be facilitated by increased interdisciplinary cooperation and open research data generation.

Determination of Gait Events and Temporal Gait Parameters for Persons with a Knee-Ankle-Foot Orthosis

Gait event detection is essential for controlling an orthosis and assessing the patient's gait. In this study, patients wearing an electromechanical (EM) knee-ankle-foot orthosis (KAFO) with a single IMU embedded in the thigh were subjected to gait event detection. The algorithm detected four essential gait events (initial contact (IC), toe off (TO), opposite initial contact (OIC), and opposite toe off (OTO)) and determined important temporal gait parameters such as stance/swing time, symmetry, and single/double limb support. These gait events were evaluated through gait experiments using four force plates on healthy adults and a hemiplegic patient who wore a one-way clutch KAFO and a pneumatic cylinder KAFO. Results showed that the smallest error in gait event detection was found at IC, and the largest error rate was observed at opposite toe off (OTO) with an error rate of -2.8 ± 1.5% in the patient group. Errors in OTO detection resulted in the largest error in determining the single limb support of the patient with an error of 5.0 ± 1.5%. The present study would be beneficial for the real-time continuous monitoring of gait events and temporal gait parameters for persons with an EM KAFO.

The Analytical Validity of Stride Detection and Gait Parameters Reconstruction Using the Ankle-Mounted Inertial Measurement Unit Syde®

The increasing use of inertial measurement units (IMU) in biomedical sciences brings new possibilities for clinical research. The aim of this paper is to demonstrate the accuracy of the IMU-based wearable Syde® device, which allows day-long and remote continuous gait recording in comparison to a reference motion capture system. Twelve healthy subjects (age: 23.17 ± 2.04, height: 174.17 ± 6.46 cm) participated in a controlled environment data collection and performed a series of gait tasks with both systems attached to each ankle. A total of 2820 strides were analyzed. The results show a median absolute stride length error of 1.86 cm between the IMU-based wearable device reconstruction and the motion capture ground truth, with the 75th percentile at 3.24 cm. The median absolute stride horizontal velocity error was 1.56 cm/s, with the 75th percentile at 2.63 cm/s. With a measurement error to the reference system of less than 3 cm, we conclude that there is a valid physical recovery of stride length and horizontal velocity from data collected with the IMU-based wearable Syde® device.

Valorization of Food Waste: Utilizing Natural Porous Materials Derived from Pomelo-Peel Biomass to Develop Triboelectric Nanogenerators for Energy Harvesting and Self-Powered Sensing

Food waste is an enormous challenge, with implications for the environment, society, and economy. Every year around the world, 1.3 billion tons of food are wasted or lost, and food waste-associated costs are around $2.6 trillion. Waste upcycling has been shown to mitigate these negative impacts. This study's optimized pomelo-peel biomass-derived porous material-based triboelectric nanogenerator (PP-TENG) had an open circuit voltage of 58 V and a peak power density of 254.8 mW/m2. As porous structures enable such triboelectric devices to respond sensitively to external mechanical stimuli, we tested our optimized PP-TENG's ability to serve as a self-powered sensor of biomechanical motions. As well as successfully harvesting sufficient mechanical energy to power light-emitting diodes and portable electronics, our PP-TENGs successfully monitored joint motions, neck movements, and gait patterns, suggesting their strong potential for use in healthcare monitoring and physical rehabilitation, among other applications. As such, the present work opens up various new possibilities for transforming a prolific type of food waste into value-added products and thus could enhance long-term sustainability while reducing such waste.

AI-Assisted Insole Sensing System for Multifunctional Plantar-Healthcare Applications

The pervasive global issue of population aging has led to a growing demand for health monitoring, while the advent of electronic wearable devices has greatly alleviated the strain on the industry. However, these devices come with inherent limitations, such as electromagnetic radiation, complex structures, and high prices. Herein, a Solaris silicone rubber-integrated PMMA polymer optical fiber (S-POF) intelligent insole sensing system has been developed for remote, portable, cost-effective, and real-time gait monitoring. The system is capable of sensitively converting the pressure of key points on the sole into changes in light intensity with correlation coefficients of 0.995, 0.952, and 0.910. The S-POF sensing structure demonstrates excellent durability with a 4.8% variation in output after 10,000 cycles and provides stable feedback for bending angles. It also exhibits water resistance and temperature resistance within a certain range. Its multichannel multiplexing framework allows a smartphone to monitor multiple S-POF channels simultaneously, meeting the requirements of convenience for daily care. Also, the system can efficiently and accurately provide parameters such as pressure, step cadence, and pressure distribution, enabling the analysis of gait phases and patterns with errors of only 4.16% and 6.25% for the stance phase (STP) and the swing phase (SWP), respectively. Likewise, after comparing various AI models, an S-POF channel-based gait pattern recognition technique has been proposed with a high accuracy of up to 96.87%. Such experimental results demonstrate that the system is promising to further promote the development of rehabilitation and healthcare.

mCrutch: A Novel m-Health Approach Supporting Continuity of Care

This paper reports the architecture of a low-cost smart crutches system for mobile health applications. The prototype is based on a set of sensorized crutches connected to a custom Android application. Crutches were instrumented with a 6-axis inertial measurement unit, a uniaxial load cell, WiFi connectivity, and a microcontroller for data collection and processing. Crutch orientation and applied force were calibrated with a motion capture system and a force platform. Data are processed and visualized in real-time on the Android smartphone and are stored on the local memory for further offline analysis. The prototype's architecture is reported along with the post-calibration accuracy for estimating crutch orientation (5° RMSE in dynamic conditions) and applied force (10 N RMSE). The system is a mobile-health platform enabling the design and development of real-time biofeedback applications and continuity of care scenarios, such as telemonitoring and telerehabilitation.

Development and Assessment of Artificial Intelligence-Empowered Gait Monitoring System Using Single Inertial Sensor

Gait instability is critical in medicine and healthcare, as it has associations with balance disorder and physical impairment. With the development of sensor technology, despite the fact that numerous wearable gait detection and recognition systems have been designed to monitor users' gait patterns, they commonly spend a lot of time and effort to extract gait metrics from signal data. This study aims to design an artificial intelligence-empowered and economic-friendly gait monitoring system. A pair of intelligent shoes with a single inertial sensor and a smartphone application were developed as a gait monitoring system to detect users' gait cycle, stand phase time, swing phase time, stride length, and foot clearance. We recruited 30 participants (24.09 ± 1.89 years) to collect gait data and used the Vicon motion capture system to verify the accuracy of the gait metrics. The results show that the gait monitoring system performs better on the assessment of the gait metrics. The accuracy of stride length and foot clearance is 96.17% and 92.07%, respectively. The artificial intelligence-empowered gait monitoring system holds promising potential for improving gait analysis and monitoring in the medical and healthcare fields.

Gait Monitoring and Analysis: A Mathematical Approach

Gait abnormalities are common in the elderly and individuals diagnosed with Parkinson's, often leading to reduced mobility and increased fall risk. Monitoring and assessing gait patterns in these populations play a crucial role in understanding disease progression, early detection of motor impairments, and developing personalized rehabilitation strategies. In particular, by identifying gait irregularities at an early stage, healthcare professionals can implement timely interventions and personalized therapeutic approaches, potentially delaying the onset of severe motor symptoms and improving overall patient outcomes. In this paper, we studied older adults affected by chronic diseases and/or Parkinson's disease by monitoring their gait due to wearable devices that can accurately detect a person's movements. In our study, about 50 people were involved in the trial (20 with Parkinson's disease and 30 people with chronic diseases) who have worn our device for at least 6 months. During the experimentation, each device collected 25 samples from the accelerometer sensor for each second. By analyzing those data, we propose a metric for the "gait quality" based on the measure of entropy obtained by applying the Fourier transform.

Introducing the Pi-CON Methodology to Overcome Usability Deficits during Remote Patient Monitoring

The adoption of telehealth has soared, and with that the acceptance of Remote Patient Monitoring (RPM) and virtual care. A review of the literature illustrates, however, that poor device usability can impact the generated data when using Patient-Generated Health Data (PGHD) devices, such as wearables or home use medical devices, when used outside a health facility. The Pi-CON methodology is introduced to overcome these challenges and guide the definition of user-friendly and intuitive devices in the future. Pi-CON stands for passive, continuous, and non-contact, and describes the ability to acquire health data, such as vital signs, continuously and passively with limited user interaction and without attaching any sensors to the patient. The paper highlights the advantages of Pi-CON by leveraging various sensors and techniques, such as radar, remote photoplethysmography, and infrared. It illustrates potential concerns and discusses future applications Pi-CON could be used for, including gait and fall monitoring by installing an omnipresent sensor based on the Pi-CON methodology. This would allow automatic data collection once a person is recognized, and could be extended with an integrated gateway so multiple cameras could be installed to enable data feeds to a cloud-based interface, allowing clinicians and family members to monitor patient health status remotely at any time.