Unrevealing the connection between real sample analysis and analytical method. The case of cytokines.

Cytokines are compounds that belong to a special class of signaling biomolecules that are responsible for several functions in the human body, being involved in cell growth, inflammatory, and neoplastic processes. Thus, they represent valuable biomarkers for diagnosing and drug therapy monitoring certain medical conditions. Because cytokines are secreted in the human body, they can be detected in both conventional samples, such as blood or urine, but also in samples less used in medical practice such as sweat or saliva. As the importance of cytokines was identified, various analytical methods for their determination in biological fluids were reported. The gold standard in cytokine detection is considered the enzyme-linked immunosorbent assay method and the most recent ones have been considered and compared in this study. It is known that the conventional methods are accompanied by a few disadvantages that new methods of analysis, especially electrochemical sensors, are trying to overcome. Electrochemical sensors proved to be suited for the elaboration of integrated, portable, and wearable sensing devices, which could also facilitate cytokines determination in medical practice.

Differential components of bradykinesia in Parkinson's disease revealed by deep brain stimulation.

Bradykinesia is a term describing several manifestations of movement disruption caused by Parkinson's disease (PD), including movement slowing, amplitude reduction, and gradual decrease of speed and amplitude over multiple repetitions of the same movement. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) improves bradykinesia in patients with PD. We examined the effect of DBS on specific components of bradykinesia when applied at two locations within the STN, using signal processing techniques to identify the time course of amplitude and frequency of repeated hand pronation-supination movements performed by participants with and without PD. Stimulation at either location increased movement amplitude, increased frequency, and decreased variability, though not to the range observed in the control group. Amplitude and frequency showed decrement within trials, which was similar in PD and control groups and did not change with DBS. Decrement across trials, by contrast, differed between PD and control groups, and was reduced by stimulation. We conclude that DBS improves specific aspects of movement that are disrupted by PD, whereas it does not affect short-term decrement that could reflect muscular fatigue.NEW & NOTEWORTHY In this study, we examined different components of bradykinesia in patients with Parkinson's disease (PD). We identified different components through signal processing techniques and their response to deep brain stimulation (DBS). We found that some components of bradykinesia respond to stimulation, whereas others do not. This knowledge advances our understanding of brain mechanisms that control movement speed and amplitude.

Joint associations of diet and physical activity with incident type 2 diabetes and hypertension: an analysis of 144,288 UK Biobank participants.

BACKGROUND: Studies examining the joint associations of lifestyle exposures can reveal novel synergistic and joint effects, but no study has examined the joint association of diet and physical activity (PA) with type 2 diabetes (T2D) and hypertension. The aim of this study is to examine the joint associations of PA and diet with incidence of type T2D and hypertension, as a combined outcome and separately in a large sample of UK adults. METHODS: This prospective cohort study included 144,288 UK Biobank participants aged 40-69. Moderate to vigorous PA (MVPA) was measured using the International Physical Activity Questionnaire and a wrist accelerometer. We categorised PA and diet indicators (diet quality score (DQS) and energy intake (EI)) based on tertiles and derived joint PA and diet variables. Outcome was major cardiometabolic disease incidence (combination of T2D and hypertension). RESULTS: A total of 14,003(7.1%) participants developed T2D, 28,075(19.2%) developed hypertension, and 30,529(21.2%) developed T2D or hypertension over a mean follow-up of 10.9(3.7) years. Participants with middle and high self-reported MVPA levels had lower risk of major cardiometabolic disease regardless of diet, e.g. among high DQS group, hazard ratios in middle and high MVPA group were 0.90 (95%CI:0.86-0.94), and 0.88(95%CI:0.84-0.92), respectively. Participants with jointly high device-measured MVPA and high DQS levels had lower major cardiometabolic disease risk (HR: 0.84, 95%CI:0.71-0.99). The equivalent joint device-measured MVPA and EI exposure analyses showed no clear pattern of associations with the outcomes. CONCLUSION: Higher PA is an important component in cardiometabolic disease prevention across all diet quality and total EI groups. The observed lack of association between diet health outcomes may stem from a lower DQS.

Ultrastretchable E-Skin Based on Conductive Hydrogel Microfibers for Wearable Sensors.

Conductive microfibers play a significant role in the flexibility, stretchability, and conductivity of electronic skin (e-skin). Currently, the fabrication of conductive microfibers suffers from either time-consuming and complex operations or is limited in complex fabrication environments. Thus, it presents a one-step method to prepare conductive hydrogel microfibers based on microfluidics for the construction of ultrastretchable e-skin. The microfibers are achieved with conductive MXene cores and hydrogel shells, which are solidified with the covalent cross-linking between sodium alginate and calcium chloride, and mechanically enhanced by the complexation reaction of poly(vinyl alcohol) and sodium hydroxide. The microfiber conductivities are tailorable by adjusting the flow rate and concentration of core and shell fluids, which is essential to more practical applications in complex scenarios. More importantly, patterned e-skin based on conductive hydrogel microfibers can be constructed by combining microfluidics with 3D printing technology. Because of the great advantages in mechanical and electrical performance of the microfibers, the achieved e-skin shows impressive stretching and sensitivity, which also demonstrate attractive application values in motion monitoring and gesture recognition. These characteristics indicate that the ultrastretchable e-skin based on conductive hydrogel microfibers has great potential for applications in health monitoring, wearable devices, and smart medicine.

Cation-π Interactions Based Conductive Hydrogels with Slide-Ring Structure Toward Super Long-Time in-air/Underwater Linear Sensing and Communication.

Conductive hydrogels (CHs) are attracted more attention in the flexible wearable sensors field, however, how to stably apply CHs underwater is still a big challenge. In order to achieve the usage of CHs in aquatic environments, the integrated properties such as water retention ability, resistance to swelling, toughness, adhesiveness, linear GF sensing, and long-term usage are necessary to consider, but rarely reported in the previous reports. This paper proposes CHs prepared using cationic and aromatic monomers along with polyrotaxanes-based crosslinkers. Due to the intermolecular cation-π interactions and topological slide-ring-based polyrotaxanes, the CHs exhibit good mechanical performance, adhesive nature, and anti-swelling properties. The presence of slide-ring-based topological architecture effectively mitigates stress concentration. Additionally, the encapsulation of PA allows CHs to maintain functionality even after 240 days of direct placement at room temperature. Notably, the designed CHs exhibit linear sensitivity in detecting land/underwater human motions, and serve as Morse code signal transmitters for information transmission. Thus, the designed CHs may have broad applications in the underwater wearable sensors field.

Graphene-Infused Sustainable Rubber-Based Triboelectric Nanogenerator For Real-Time Human Motion Monitoring.

Triboelectric nanogenerators (TENG) are promising alternatives for clean energy harvesting. However, the material utilization in the development of TENG relies majorly on polymers derived from non-renewable resources. Therefore, minimizing the carbon footprint associated with such TENG development demands a shift toward usage of sustainable materials. This study pioneers using natural rubber (NR) as a sustainable alternative in TENG development. Infusing graphene in NR, its dielectric constant and tribonegativity are optimized, yielding a remarkable enhancement. The optimized sample exhibits a dielectric constant of 411 (at 103 Hz) and a contact potential difference (CPD) value of 1.85 V. In contrast, the pristine NR sample showed values of 6 and 3.06 V for the dielectric constant and CPD. Simulation and experimental studies fine-tune the TENG's performance, demonstrating excellent agreement between theoretical predictions and practical studies. Sensors developed via stencil printing technique possess a remarkably low layer thickness of 270 µm, and boast a power density of 420 mW m-2, a staggering 250% increase over conventional NR. Moreover, the material is pressure sensitive, enabling precise real-time human motion detection, including finger contact, finger bending, neck bending, and arm bending. This versatile sensor offers wireless monitoring, empowering healthcare monitoring based on the Internet of Things.

Wearable sensor supports in-situ and continuous monitoring of plant health in precision agriculture era.

Plant health is intricately linked to crop quality, food security and agricultural productivity. Obtaining accurate plant health information is of paramount importance in the realm of precision agriculture. Wearable sensors offer an exceptional avenue for investigating plant health status and fundamental plant science, as they enable real-time and continuous in-situ monitoring of physiological biomarkers. However, a comprehensive overview that integrates and critically assesses wearable plant sensors across various facets, including their fundamental elements, classification, design, sensing mechanism, fabrication, characterization and application, remains elusive. In this study, we provide a meticulous description and systematic synthesis of recent research progress in wearable sensor properties, technology and their application in monitoring plant health information. This work endeavours to serve as a guiding resource for the utilization of wearable plant sensors, empowering the advancement of plant health within the precision agriculture paradigm.

Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease.

BACKGROUND: Real-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression. OBJECTIVES: The aim was to identify real-world mobility measures that reflect PD severity and show discriminant ability and sensitivity to disease progression, compared to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scale. METHODS: Multicenter real-world continuous (24/7) digital mobility data from 587 persons with PD and 68 matched healthy controls were collected using an accelerometer adhered to the lower back. Machine learning feature selection and regression algorithms evaluated associations of the digital measures using the MDS-UPDRS (I-III). Binary logistic regression assessed discriminatory value using controls, and longitudinal observational data from a subgroup (n = 33) evaluated sensitivity to change over time. RESULTS: Digital measures were only moderately correlated with the MDS-UPDRS (part II-r = 0.60 and parts I and III-r = 0.50). Most associated measures reflected activity quantity and distribution patterns. A model with 14 digital measures accurately distinguished recently diagnosed persons with PD from healthy controls (81.1%, area under the curve: 0.87); digital measures showed larger effect sizes (Cohen's d: [0.19-0.66]), for change over time than any of the MDS-UPDRS parts (Cohen's d: [0.04-0.12]). CONCLUSIONS: Real-world mobility measures are moderately associated with clinical assessments, suggesting that they capture different aspects of motor capacity and function. Digital mobility measures are sensitive to early-stage disease and to disease progression, to a larger degree than conventional clinical assessments, demonstrating their utility, primarily for clinical trials but ultimately also for clinical care. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Standing Balance Conditions and Digital Sway Measures for Clinical Trials of Friedreich's Ataxia.

BACKGROUND: Progressive loss of standing balance is a feature of Friedreich's ataxia (FRDA). OBJECTIVES: This study aimed to identify standing balance conditions and digital postural sway measures that best discriminate between FRDA and healthy controls (HC). We assessed test-retest reliability and correlations between sway measures and clinical scores. METHODS: Twenty-eight subjects with FRDA and 20 HC completed six standing conditions: feet apart, feet together, and feet tandem, both with eyes opened (EO) and eyes closed. Sway was measured using a wearable sensor on the lumbar spine for 30 seconds. Test completion rate, test-retest reliability with intraclass correlation coefficients, and areas under the receiver operating characteristic curves (AUCs) for each measure were compared to identify distinguishable FRDA sway characteristics from HC. Pearson correlations were used to evaluate the relationships between discriminative measures and clinical scores. RESULTS: Three of the six standing conditions had completion rates over 70%. Of these three conditions, natural stance and feet together with EO showed the greatest completion rates. All six of the sway measures' mean values were significantly different between FRDA and HC. Four of these six measures discriminated between groups with >0.9 AUC in all three conditions. The Friedreich Ataxia Rating Scale Upright Stability and Total scores correlated with sway measures with P-values <0.05 and r-values (0.63-0.86) and (0.65-0.81), respectively. CONCLUSION: Digital postural sway measures using wearable sensors are discriminative and reliable for assessing standing balance in individuals with FRDA. Natural stance and feet together stance with EO conditions suggest use in clinical trials for FRDA. © 2024 International Parkinson and Movement Disorder Society.

Digital Measures of Postural Sway Quantify Balance Deficits in Spinocerebellar Ataxia.

BACKGROUND: Maintaining balance is crucial for independence and quality of life. Loss of balance is a hallmark of spinocerebellar ataxia (SCA). OBJECTIVE: The aim of this study was to identify which standing balance conditions and digital measures of body sway were most discriminative, reliable, and valid for quantifying balance in SCA. METHODS: Fifty-three people with SCA (13 SCA1, 13 SCA2, 14 SCA3, and 13 SCA6) and Scale for Assessment and Rating of Ataxia (SARA) scores 9.28 ± 4.36 and 31 healthy controls were recruited. Subjects stood in six test conditions (natural stance, feet together and tandem, each with eyes open [EO] and eyes closed [EC]) with an inertial sensor on their lower back for 30 seconds (×2). We compared test completion rate, test-retest reliability, and areas under the receiver operating characteristic curve (AUC) for seven digital sway measures. Pearson's correlations related sway with the SARA and the Patient-Reported Outcome Measure of Ataxia (PROM ataxia). RESULTS: Most individuals with SCA (85%-100%) could stand for 30 seconds with natural stance EO or EC, and with feet together EO. The most discriminative digital sway measures (path length, range, area, and root mean square) from the two most reliable and discriminative conditions (natural stance EC and feet together EO) showed intraclass correlation coefficients from 0.70 to 0.91 and AUCs from 0.83 to 0.93. Correlations of sway with SARA were significant (maximum r = 0.65 and 0.73). Correlations with PROM ataxia were mild to moderate (maximum r = 0.56 and 0.34). CONCLUSION: Inertial sensor measures of extent of postural sway in conditions of natural stance EC and feet together stance EO were discriminative, reliable, and valid for monitoring SCA. © 2024 International Parkinson and Movement Disorder Society.

Digital Gait Outcomes for Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS): Discriminative, Convergent, and Ecological Validity in a Multicenter Study (PROSPAX).

BACKGROUND: With treatment trials on the horizon, this study aimed to identify candidate digital-motor gait outcomes for autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS), capturable by wearable sensors with multicenter validity, and ideally also ecological validity during free walking outside laboratory settings. METHODS: Cross-sectional multicenter study (four centers), with gait assessments in 36 subjects (18 ARSACS patients; 18 controls) using three body-worn sensors (Opal, APDM) in laboratory settings and free walking in public spaces. Sensor gait measures were analyzed for discriminative validity from controls, and for convergent (ie, clinical and patient relevance) validity by correlations with SPRSmobility (primary outcome) and Scale for the Assessment and Rating of Ataxia (SARA), Spastic Paraplegia Rating Scale (SPRS), and activities of daily living subscore of the Friedreich Ataxia Rating Scale (FARS-ADL) (exploratory outcomes). RESULTS: Of 30 hypothesis-based digital gait measures, 14 measures discriminated ARSACS patients from controls with large effect sizes (|Cliff's δ| > 0.8) in laboratory settings, with strongest discrimination by measures of spatiotemporal variability Lateral Step Deviation (δ = 0.98), SPcmp (δ = 0.94), and Swing CV (δ = 0.93). Large correlations with the SPRSmobility were observed for Swing CV (Spearman's ρ = 0.84), Speed (ρ = -0.63), and Harmonic Ratio V (ρ = -0.62). During supervised free walking in a public space, 11/30 gait measures discriminated ARSACS from controls with large effect sizes. Large correlations with SPRSmobility were here observed for Swing CV (ρ = 0.78) and Speed (ρ = -0.69), without reductions in effect sizes compared with laboratory settings. CONCLUSIONS: We identified a promising set of digital-motor candidate gait outcomes for ARSACS, applicable in multicenter settings, correlating with patient-relevant health aspects, and with high validity also outside laboratory settings, thus simulating real-life walking with higher ecological validity. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Comparing Cerebellar tDCS and Cerebellar tACS in Neurodegenerative Ataxias Using Wearable Sensors: A Randomized, Double-Blind, Sham-Controlled, Triple-Crossover Trial.

Cerebellar transcranial direct current stimulation (tDCS) represents a promising therapeutic approach for both motor and cognitive symptoms in neurodegenerative ataxias. Recently, transcranial alternating current stimulation (tACS) was also demonstrated to modulate cerebellar excitability by neuronal entrainment. To compare the effectiveness of cerebellar tDCS vs. cerebellar tACS in patients with neurodegenerative ataxia, we performed a double-blind, randomized, sham controlled, triple cross-over trial with cerebellar tDCS, cerebellar tACS or sham stimulation in twenty-six participants with neurodegenerative ataxia. Before entering the study, each participant underwent motor assessment with wearable sensors considering gait cadence (steps/minute), turn velocity (degrees/second) and turn duration (seconds), and a clinical evaluation with the scale for the Assessment and Rating of Ataxia (SARA) and the International Cooperative Ataxia Rating Scale (ICARS). After each intervention, participants underwent the same clinical assessment along with cerebellar inhibition (CBI) measurement, a marker of cerebellar activity. The gait cadence, turn velocity, SARA, and ICARS significantly improved after both tDCS and tACS, compared to sham stimulation (all p<0.010). Comparable effects were observed for CBI (p<0.001). Overall, tDCS significantly outperformed tACS on clinical scales and CBI (p<0.01). A significant correlation between changes of wearable sensors parameters from baseline and changes of clinical scales and CBI scores was detected. Cerebellar tDCS and cerebellar tACS are effective in ameliorating symptoms of neurodegenerative ataxias, with the former being more beneficial than the latter. Wearable sensors may serve as rater-unbiased outcome measures in future clinical trials. ClinicalTrial.gov Identifier: NCT05621200.

Understanding the influence of context on real-world walking energetics.

Speeds that minimize energetic cost during steady-state walking have been observed during lab-based investigations of walking biomechanics and energetics. However, in real-world scenarios, humans walk in a variety of contexts that can elicit different walking strategies, and may not always prioritize minimizing energetic cost. To investigate whether individuals tend to select energetically optimal speeds in real-world situations and how contextual factors influence gait, we conducted a study combining data from lab and real-world experiments. Walking kinematics and context were measured during daily life over a week (N=17) using wearable sensors and a mobile phone. To determine context, we utilized self-reported activity logs, GPS data and follow-up exit interviews. Additionally, we estimated energetic cost using respirometry over a range of gait speeds in the lab. Gross and net cost of transport were calculated for each participant, and were used to identify energetically optimal walking speed ranges for each participant. The proportion of real-world steady-state stride speeds within these ranges (gross and net) were identified for all data and for each context. We found that energetically optimal speeds predicted by gross cost of transport were more predictive of walking speeds used during daily life than speeds that would minimize net cost of transport. On average, 82.2% of all steady-state stride speeds were energetically optimal for gross cost of transport for all contexts and participants, while only 45.6% were energetically optimal for net cost of transport. These results suggest that while energetic cost is a factor considered by humans when selecting gait speed in daily life, it is not the sole determining factor. Context contributes to the observed variability in movement parameters both within and between individuals.

Capacitive spectroscopy as transduction mechanism for wearable biosensors: opportunities and challenges.

Wearable sensors would revolutionize healthcare and personalized medicine by providing individuals with continuous and real-time data about their bodies and environments. Their integration into everyday life has the potential to enhance well-being, improve healthcare outcomes, and offer new opportunities for research. Capacitive sensors technology has great potential to enrich wearable devices, extending their use to more accurate physiological indicators. On the basis of capacitive sensors developed so far to monitor physical parameters, and taking into account the advances in capacitive biosensors, this work discusses the benefits of this type of transduction to design wearables for the monitoring of biomolecules. Moreover, it provides insights into the challenges that must be overcome to take advantage of capacitive transduction in wearable sensors for health.

Environment Endurable, Self-Healing, Super-Adhesive, and Mechanically Strong Ionogels for Reliable Sensing.

Ionogels possess high conductivity, stretchability, and adhesion, making them promising as flexible sensors. However, it remains challenging to fabricate an ionogel which integrates excellent environment endurance, superior mechanical strength, high self-healing efficiency, and super adhesion. Herein, a supramolecular ionic liquid is synthesized using calcium chloride and 1-butyl-3-methylimidazolium chloride. An advanced ionogel based on this supramolecular ionic liquid is conveniently constructed by a one-pot method with acrylamide and acrylic acid as monomers. The supramolecular cross-linking network, formed by affluent coordination interactions, hydrogen bonds, and electrostatic interactions, provides the ionogel with ideal mechanical strength (tensile strength up to 1.7 MPa), high self-healing efficiency (up to 149%), super adhesion (up to 358 kPa on aluminum), excellent solvent tolerance (less than 10% weight increase, high mechanical and sensing performance retention after being soaked in organic solvents), and low-temperature endurance (breaking elongation can reach 87% at -30 °C). The supramolecular ionogels can function as multi-mode sensors, capable of monitoring strain and different amplitudes of human movements in real-time. Moreover, the sensing performance of ionogels remains unaffected even after being self-healed or exposure to organic solvents. It is expected that this study could offer valuable design ideas to construct advanced gel materials applicable in complicated environment.

Applications of Functional Polymeric Eutectogels.

Over the past two decades, deep eutectic solvents (DESs) have captured significant attention as an emergent class of solvents that have unique properties and applications in differing fields of chemistry. One area where DES systems find utility is the design of polymeric gels, often referred to as "eutectogels," which can be prepared either using a DES to replace a traditional solvent, or where monomers form part of the DES themselves. Due to the extensive network of intramolecular interactions (e.g., hydrogen bonding) and ionic species that exist in DES systems, polymeric eutectogels often possess appealing material properties-high adhesive strength, tuneable viscosity, rapid polymerization kinetics, good conductivity, as well as high strength and flexibility. In addition, non-covalent crosslinking approaches are possible due to the inherent interactions that exist in these materials. This review considers several key applications of polymeric eutectogels, including organic electronics, wearable sensor technologies, 3D printing resins, adhesives, and a range of various biomedical applications. The design, synthesis, and properties of these eutectogels are discussed, in addition to the advantages of this synthetic approach in comparison to traditional gel design. Perspectives on the future directions of this field are also highlighted.

Effect of test duration and sensor location on the reliability of standing balance parameters derived using body-mounted accelerometers.

BACKGROUND: Balance parameters derived from wearable sensor measurements during postural sway have been shown to be sensitive to experimental variables such as test duration, sensor number, and sensor location that influence the magnitude and frequency-related properties of measured center-of-mass (COM) and center-of-pressure (COP) excursions. In this study, we investigated the effects of test duration, the number of sensors, and sensor location on the reliability of standing balance parameters derived using body-mounted accelerometers. METHODS: Twelve volunteers without any prior history of balance disorders were enrolled in the study. They were asked to perform two 2-min quiet standing tests with two different testing conditions (eyes open and eyes closed). Five inertial measurement units (IMUs) were employed to capture postural sway data from each participant. IMUs were attached to the participants' right legs, the second sacral vertebra, sternum, and the left mastoid processes. Balance parameters of interest were calculated for the single head, sternum, and sacrum accelerometers, as well as, a three-sensor combination (leg, sacrum, and sternum). Accelerometer data were used to estimate COP-based and COM-based balance parameters during quiet standing. To examine the effect of test duration and sensor location, each 120-s recording from different sensor locations was segmented into 20-, 30-, 40-, 50-, 60-, 70-, 80-, 90-, 100-, and 110-s intervals. For each of these time intervals, time- and frequency-domain balance parameters were calculated for all sensor locations. RESULTS: Most COM-based and COP-based balance parameters could be derived reliably for clinical applications (Intraclass-Correlation Coefficient, ICC ≥ 0.90) with a minimum test duration of 70 and 110 s, respectively. The exceptions were COP-based parameters obtained using a sacrum-mounted sensor, especially in the eyes-closed condition, which could not be reliably used for clinical applications even with a 120-s test duration. CONCLUSIONS: Most standing balance parameters can be reliably measured using a single head- or sternum-mounted sensor within a 120-s test duration. For other sensor locations, the minimum test duration may be longer and may depend on the specific test conditions.

Deep neural networks for wearable sensor-based activity recognition in Parkinson's disease: investigating generalizability and model complexity.

BACKGROUND: The research gap addressed in this study is the applicability of deep neural network (NN) models on wearable sensor data to recognize different activities performed by patients with Parkinson's Disease (PwPD) and the generalizability of these models to PwPD using labeled healthy data. METHODS: The experiments were carried out utilizing three datasets containing wearable motion sensor readings on common activities of daily living. The collected readings were from two accelerometer sensors. PAMAP2 and MHEALTH are publicly available datasets collected from 10 and 9 healthy, young subjects, respectively. A private dataset of a similar nature collected from 14 PwPD patients was utilized as well. Deep NN models were implemented with varying levels of complexity to investigate the impact of data augmentation, manual axis reorientation, model complexity, and domain adaptation on activity recognition performance. RESULTS: A moderately complex model trained on the augmented PAMAP2 dataset and adapted to the Parkinson domain using domain adaptation achieved the best activity recognition performance with an accuracy of 73.02%, which was significantly higher than the accuracy of 63% reported in previous studies. The model's F1 score of 49.79% significantly improved compared to the best cross-testing of 33.66% F1 score with only data augmentation and 2.88% F1 score without data augmentation or domain adaptation. CONCLUSION: These findings suggest that deep NN models originating on healthy data have the potential to recognize activities performed by PwPD accurately and that data augmentation and domain adaptation can improve the generalizability of models in the healthy-to-PwPD transfer scenario. The simple/moderately complex architectures tested in this study could generalize better to the PwPD domain when trained on a healthy dataset compared to the most complex architectures used. The findings of this study could contribute to the development of accurate wearable-based activity monitoring solutions for PwPD, improving clinical decision-making and patient outcomes based on patient activity levels.

Detection and prediction of freezing of gait with wearable sensors in Parkinson's disease.

Freezing of gait (FoG) is one of the most distressing symptoms of Parkinson's Disease (PD), commonly occurring in patients at middle and late stages of the disease. Automatic and accurate FoG detection and prediction have emerged as a promising tool for long-term monitoring of PD and implementation of gait assistance systems. This paper reviews the recent development of FoG detection and prediction using wearable sensors, with attention on identifying knowledge gaps that need to be filled in future research. This review searched the PubMed and Web of Science databases to collect studies that detect or predict FoG with wearable sensors. After screening, 89 of 270 articles were included. The data description, extracted features, detection/prediction methods, and classification performance were extracted from the articles. As the number of papers of this area is increasing, the performance has been steadily improved. However, small datasets and inconsistent evaluation processes still hinder the application of FoG detection and prediction with wearable sensors in clinical practice.

Prediction of instantaneous perceived effort during outdoor running using accelerometry and machine learning.

The rate of perceived effort (RPE) is a subjective scale widely used for defining training loads. However, the subjective nature of the metric might lead to an inaccurate representation of the imposed metabolic/mechanical exercise demands. Therefore, this study aimed to predict the rate of perceived exertions during running using biomechanical parameters extracted from a commercially available running smartwatch. Forty-three recreational runners performed a simulated 5-km race on a track, providing their RPE from a Borg scale (6-20) every 400 m. Running distance, heart rate, foot contact time, cadence, stride length, and vertical oscillation were extracted from a running smartwatch (Garmin 735XT). Machine learning regression models were trained to predict the RPE at every 5 s of the 5-km race using subject-independent (leave-one-out), as well as a subject-dependent regression method. The subject-dependent method was tested using 5%, 10%, or 20% of the runner's data in the training set while using the remaining data for testing. The average root-mean-square error (RMSE) in predicting the RPE using the subject-independent method was 1.8 ± 0.8 RPE points (range 0.6-4.1; relative RMSE ~ 12 ± 6%) across the entire 5-km race. However, the error from subject-dependent models was reduced to 1.00 ± 0.31, 0.66 ± 0.20 and 0.45 ± 0.13 RPE points when using 5%, 10%, and 20% of data for training, respectively (average relative RMSE < 7%). All types of predictions underestimated the maximal RPE in ~ 1 RPE point. These results suggest that the data accessible from commercial smartwatches can be used to predict perceived exertion, opening new venues to improve training workload monitoring.