Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder.

Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorders.

Effects of Strengthening Exercises on Human Kinetic Chains Based on a Systematic Review.

Kinetic chains (KCs) are primarily affected by the load of different activities that recruit muscles from different regions. We explored the effects of strengthening exercises on KCs through muscle activation. Four databases were searched from 1990 to 2019. The muscles of each KC, their surface electromyography (sEMG), and the exercises conducted were reported. We found 36 studies that presented muscle activation using the percent (%) maximal voluntary isometric contraction (MVIC) or average sEMG for nine KCs in different regions. The % MVIC is presented as the following four categories: low (≤20%), moderate (21~40%), high (41~60%), and very high (>60%). Only four studies mentioned muscle activation in more than three KCs, while the remaining studies reported inconsistent sEMG processing, lacked normalization, and muscle activation in one or two KCs. The roles of stabilizers and the base of support in overhead throwing mobility using balance exercises were examined, and the concentric phase of chin-up and lat pull-down activated the entire KC by recruiting multiple muscles. Also, deep-water running was shown to prevent the risk of falls and enhance balance and stability. In addition, low-load trunk rotations improved the muscles of the back and external oblique activation. Based on this study's findings, closed-chain exercises activate more groups of muscles in a kinetic chain than open-chain exercises. However, no closed or open chain exercise can activate optimal KCs.

Beneficial carry-over effects of chronic at-home genital nerve stimulation on incontinence in individuals with spinal cord injury: A pragmatic trial.

BACKGROUND: Genital nerve stimulation (GNS) is a promising, but under-researched, alternative treatment for neurogenic detrusor overactivity (NDO) in those with spinal cord injury (SCI). OBJECTIVES: To investigate the urodynamic, quality-of-life (QOL) and carry-over effects of GNS when applied at home for 2 weeks by participants with incomplete SCI and NDO during activities of daily living. METHODS: Seven men and 1 woman participated in this 1-month protocol study. Urodynamic and QOL data were gathered during week 1 (baseline measurements), followed by 2 weeks of daily GNS at home using a portable device. GNS was applied either on-demand or thrice daily, depending on the individual's sensation. At week 4, post-stimulation tests were repeated to record any carry-over effect from the GNS. Participants maintained voiding diaries throughout the study. Assessments were carried out at the end of each protocol period in a randomized order. Clinical procedures were conducted at Taipei Medical University Hospital (Taipei, Taiwan). RESULTS: Everyone completed the study but only 7 of the 8 participants completed their voiding diary. Two weeks after GNS, average cystometric bladder capacity was increased by 30 % compared to baseline (P< 0.05). A 1-week carry-over effect was demonstrated as this capacity remained, on average, 35 % greater than baseline in week 4 after GNS was stopped (P< 0.05). Incontinence frequency significantly decreased by the end of week 3 (P< 0.05) but no significant improvements were recorded for either detrusor pressure or bladder compliance. CONCLUSIONS: Chronic at-home GNS improved cystometric bladder capacity and reduced urinary incontinence for individuals with incomplete SCI and NDO. A carry-over effect of 1 week was observed following GNS treatment. The use of portable GNS treatment that can be applied by the individual at home merits further investigation as alternative treatment for NDO in those with SCI.

Prediction of Spasticity through Upper Limb Active Range of Motion in Stroke Survivors: A Generalized Estimating Equation Model.

BACKGROUND: We aim to study the association between spasticity and active range of motion (ROM) during four repetitive functional tasks such as cone stacking (CS), fast flexion-extension (FFE), fast ball squeezing (FBS), and slow ball squeezing (SBS), and predicted spasticity models. METHODS: An experimental study with control and stroke groups was conducted in a Medical Center. A total of sixty-four participants, including healthy control (n = 22; average age (years) = 54.68 ± 9.63; male/female = 12/10) and chronic stroke survivors (n = 42; average age = 56.83 ± 11.74; male/female = 32/10) were recruited. We employed a previously developed smart glove device mounted with multiple inertial measurement unit (IMU) sensors on the upper limbs of healthy and chronic stroke individuals. The recorded ROMs were used to predict subjective spasticity through generalized estimating equations (GEE) for the affected side. RESULTS: The models have significant (p ≤ 0.05 *) prediction of spasticity for the elbow, thumb, index, middle, ring, and little fingers. Overall, during SBS and FFE activities, the maximum number of upper limb joints attained the greater average ROMs. For large joints, the elbow during CS and the wrist during FFE have the highest average ROMs, but smaller joints and the wrist have covered the highest average ROMs during FFE, FBS, and SBS activities. CONCLUSIONS: Thus, it is concluded that CS can be used for spasticity assessment of the elbow, FFE for the wrist, and SBS, FFE, and FBS activities for the thumb and finger joints in chronic stroke survivors.

Effectiveness of Repetitive Transcranial Magnetic Stimulation Combined With Transspinal Electrical Stimulation on Corticospinal Excitability for Individuals With Incomplete Spinal Cord Injury: A Pilot Study.

Repetitive Transcranial Magnetic Stimulation (rTMS) and transspinal electrical stimulation (tsES) have been proposed as a novel neurostimulation modality for individuals with incomplete spinal cord injury (iSCI). In this study, we integrated magnetic and electrical stimulators to provide neuromodulation therapy to individuals with incomplete spinal cord injury (iSCI). We designed a clinical trial comprising an 8-week treatment period and a 4-week treatment-free observation period. Cortical excitability, clinical features, inertial measurement unit and surface electromyography were assessed every 4 weeks. Twelve individuals with iSCI were recruited and randomly divided into a combined therapy group, a magnetic stimulation group, an electrical stimulation group, or a sham stimulation group. The magnetic and electric stimulations provided in this study were intermittent theta-burst stimulation (iTBS) and 2.5-mA direct current (DC) stimulation, respectively. Combined therapy, which involves iTBS and transspinal DC stimulation (tsDCS), was more effective than was iTBS alone or tsDCS alone in terms of increasing corticospinal excitability. In conclusion, the effectiveness of 8-week combined therapy in increasing corticospinal excitability faded 4 weeks after the cessation of treatment. According to the results, combination of iTBS rTMS and tsDCS treatment was more effective than was iTBS rTMS alone or tsDCS alone in enhancing corticospinal excitability. Although promising, the results of this study must be validated by studies with longer interventions and larger sample sizes.

A Deep Learning-Based Chair System That Detects Sitting Posture.

Long-term poor sitting posture leads to physical injuries such as muscle soreness and waist and neck alignment problems. In this study, we proposed an intelligent sitting posture detection system that uses depth cameras fixed on a chair to capture depth images of the user's sitting posture, and then applies a trained artificial intelligence (AI) model on an embedded Raspberry Pi board to recognize the user's sitting posture from the image data. Finally, through Bluetooth on the Raspberry Pi, the results are sent to the user's smartphone application for display and recording to achieve rapid detection of sitting posture and warning of poor sitting posture. The contribution of this study is its use of two depth cameras mounted on a chair, thereby eliminating the problem of cumbersome sensors that compromise user comfort or are prone to damage. The detection of the user's entire sitting posture was completed on an edge computing platform, which leads to power savings and offers privacy protection. Furthermore, because of the low battery power usage, the system is portable. To perform quick AI calculations, we developed a lightweight EfficientNet model and programmed it for the Raspberry Pi. The system achieved an accuracy of 99.71% and an execution speed of almost one posture result per second.

B-Spline Modeling of Inertial Measurements for Evaluating Stroke Rehabilitation Effectiveness.

Patients who experience upper-limb paralysis after stroke require continual rehabilitation. Rehabilitation must be evaluated for appropriate treatment adjustment; such evaluation can be performed using inertial measurement units (IMUs) instead of standard scales or subjective evaluations. However, IMUs produce large quantities of discretized data, and using these data directly is challenging. In this study, B-splines were used to estimate IMU trajectory data for objective evaluations of hand function and stability by using machine learning classifiers and mathematical indices. IMU trajectory data from a 2018 study on upper-limb rehabilitation were used to validate the proposed method. Features extracted from B -spline trajectories could be used to classify individuals in the 2018 study with high accuracy, and the proposed indices revealed differences between these groups. Compared with conventional rehabilitation evaluation methods, the proposed method is more objective and effective.

System Based on Artificial Intelligence Edge Computing for Detecting Bedside Falls and Sleep Posture.

Bedside falls and pressure ulcers are crucial issues in geriatric care. Although many bedside monitoring systems have been proposed, they are limited by the computational complexity of their algorithms. Moreover, most of the data collected by the sensors of these systems must be transmitted to a back-end server for calculation. With an increase in the demand for the Internet of Things, problems such as higher cost of bandwidth and overload of server computing are faced when using the aforementioned systems. To reduce the server workload, certain computing tasks must be offloaded from cloud servers to edge computing platforms. In this study, a bedside monitoring system based on neuromorphic computing hardware was developed to detect bedside falls and sleeping posture. The artificial intelligence neural network executed on the back-end server was simplified and used on an edge computing platform. An integer 8-bit-precision neural network model was deployed on the edge computing platform to process the thermal image captured by the thermopile array sensing element to conduct sleep posture classification and bed position detection. The bounding box of the bed was then converted into the features for posture classification correction to correct the posture. In an experimental evaluation, the accuracy rate, inferencing speed, and power consumption of the developed system were 94.56%, 5.28 frames per second, and 1.5 W, respectively. All the calculations of the developed system are conducted on an edge computing platform, and the developed system only transmits fall events to the back-end server through Wi-Fi and protects user privacy.

Novel Smart Assistance System for Arteriosclerosis Evaluation.

Arteriosclerosis is a cardiovascular disease that can cause calcification, sclerosis, stenosis, or obstruction of blood vessels and may further cause abnormal peripheral blood perfusion or other complications. In clinical settings, several approaches, such as computed tomography angiography and magnetic resonance angiography, can be used to evaluate arteriosclerosis status. However, these approaches are relatively expensive and require an experienced operator and often the injection of a contrast agent. In this article, a novel smart assistance system based on near-infrared spectroscopy was proposed that can noninvasively assess blood perfusion and thus indicate arteriosclerosis status. In this system, a wireless peripheral blood perfusion monitoring device simultaneously monitors changes in hemoglobin parameters and the cuff pressure applied by a sphygmomanometer. Several indexes extracted from changes in hemoglobin parameters and cuff pressure were defined and can be used to estimate blood perfusion status. A neural network model for arteriosclerosis evaluation was constructed using the proposed system. The relationship between the blood perfusion indexes and arteriosclerosis status was investigated, and the neural network model for arteriosclerosis evaluation was validated. Experimental results indicated that the differences in many blood perfusion indexes for different groups were significant and that the neural network model could effectively evaluate arteriosclerosis status (accuracy = 80.26%). By using a sphygmomanometer, the model can be employed for simple arteriosclerosis screening and blood pressure measurements. The model offers real-time noninvasive measurement, and the system is relatively inexpensive and easy to operate.

Intelligent Headband System for Evaluating Rehabilitation Effectiveness.

Stroke is an acute cerebrovascular condition causing damage to cranial nerves and requires subsequent rehabilitation treatment. In clinical practice, the effectiveness of rehabilitation is usually subjectively assessed by experienced physicians or using global prognostic scales. Several brain imaging techniques, such as positron emission tomography, functional magnetic resonance imaging, and computed tomography angiography, can be applied in rehabilitation effectiveness evaluation, but their complexity and long measurement times limit the activity of patients during measurement. This paper proposes an intelligent headband system based on near-infrared spectroscopy. An optical headband continuously and noninvasively monitors changes in hemoglobin parameters in the brain. The system's wearable headband and wireless transmission provide convenience of use. According to the change of hemoglobin parameters during rehabilitation exercise, several indexes were also defined to evaluate the state of cardiopulmonary function and further build the neural network model of the cardiopulmonary function evaluation. Finally, the relationship between the defined indexes and the cardiopulmonary function state were investigated and the neural network model for the cardiopulmonary function evaluation was also applied in the rehabilitation effect evaluation. The experimental results show the cardiopulmonary function state could reflect on most of the defined indexes and the output of neural network model, and the rehabilitation therapy could also improve the cardiopulmonary function.

A time-course study of urodynamic analyses in rat models with dopaminergic depletion induced through unilateral and bilateral 6-hydroxydopamine injections.

BACKGROUND: Bladder dysfunction is a common non-motor disorder in Parkinson's disease (PD). This study attempted to determine the bladder dysfunction with disease progression in the PD rat model produced from unilateral/bilateral injections of 6-hydroxydopamine (6-OHDA). METHODS: Cystometrographic (CMG) and external urethral sphincter electromyographic (EUS-EMG) measurements were scheduled in a time-course manner to determine the disease timing, onset, and severity. Animals were allotted into normal control, unilateral, bilateral 6-OHDA injected groups and subjected to scheduled CMG, EUS-EMG analyses at weeks 1, 2, and 4. RESULTS: The urodynamic results concluded that voiding efficiency (VE) was reduced in both unilateral and bilateral PD rats at all-time points. VE had decreased from 57 ± 11% to 31 ± 7% in unilateral PD rats and in bilateral PD rats, a decreased VE of 20 ± 6% was observed compared to control and unilateral PD rats. The EMG results in unilateral PD rats indicated declines in bursting period (BP) (3.78-2.94 s), active period (AP) (93.38-88.75 ms), and silent period (SP) (161.62-114.30 ms). A sudden reduction was noticed in BP (3.62-2.82 s), AP (92.21-86.01 ms), and SP (128.61-60.16 ms) of bilateral PD rats than in control and unilateral PD rats. Histological evidence exhibited a progressive dopaminergic neurons (DA) depletion in the substantia nigra (SN) region in 6-OHDA lesioned rats. CONCLUSION: The experimental outcomes strongly implied that significant variations in bladder function and VE decline were due to the depletion of DA neurons in the SN region of the brain.

AI-Based Automatic System for Assessing Upper-Limb Spasticity of Patients with Stroke Through Voluntary Movement.

Spasticity is a common complication for patients with stroke, but only few studies investigate the relation between spasticity and voluntary movement. This study proposed a novel automatic system for assessing the severity of spasticity (SS) of four upper-limb joints, including the elbow, wrist, thumb, and fingers, through voluntary movements. A wearable system which combined 19 inertial measurement units and a pressure ball was proposed to collect the kinematic and force information when the participants perform four tasks, namely cone stacking (CS), fast flexion and extension (FFE), slow ball squeezing (SBS), and fast ball squeezing (FBS). Several time and frequency domain features were extracted from the collected data, and two feature selection approaches based on recursive feature elimination were adopted to select the most influential features. The selected features were input into five machine learning techniques for assessing the SS for each joint. The results indicated that using CS task to assess the SS of elbow and fingers and using FBS task to assess the SS of thumb and wrist can reach the highest weighted-average F1-score. Furthermore, the study also concluded that FBS is the optimal task for assessing all the four upper-limb joints. The overall result shown that the proposed automatic system can assess four upper-limb joints through voluntary movements accurately, which is a breakthrough of finding the relation between spasticity and voluntary movement.

Design of a Multi-Sensor System for Exploring the Relation between Finger Spasticity and Voluntary Movement in Patients with Stroke.

A novel wearable multi-sensor data glove system is developed to explore the relation between finger spasticity and voluntary movement in patients with stroke. Many stroke patients suffer from finger spasticity, which is detrimental to their manual dexterity. Diagnosing and assessing the degrees of spasticity require neurological testing performed by trained professionals to estimate finger spasticity scores via the modified Ashworth scale (MAS). The proposed system offers an objective, quantitative solution to assess the finger spasticity of patients with stroke and complements the manual neurological test. In this work, the hardware and software components of this system are described. By requiring patients to perform five designated tasks, biomechanical measurements including linear and angular speed, acceleration, and pressure at every finger joint and upper limb are recorded, making up more than 1000 features for each task. We conducted a preliminary clinical test with 14 subjects using this system. Statistical analysis is performed on the acquired measurements to identify a small subset of features that are most likely to discriminate a healthy patient from patients suffering from finger spasticity. This encouraging result validates the feasibility of this proposed system to quantitatively and objectively assess finger spasticity.

Development of a Smartphone-Based mHealth Platform for Telerehabilitation.

Telerehabilitation is becoming increasingly valuable as a method for expanding medical services. The smartphone-based mHealth platform (SMPT) has been developed to provide high-quality remote rehabilitation through a smartphone and inertial measurement units. The SMPT uses smartphone as a main platform with connection to medical backend server to provide telerehabilitation. Patients would be referred to therapists to receive a tutorial of exercise technique prior to conducting their home exercise. Once patients begin their home exercises, they can report any problems instantly through the SMPT. The medical staff can adjust the exercise program according to patient feedback and the data collected by the SMPT. After completing the exercise program, patients visit their clinician for re-evaluation. A Service User Technology Acceptability Questionnaire from both medical professional and public perspective revealed a high level of agreement on enhanced care, increased accessibility, and satisfaction and a moderate level of agreement on the use of this platform as a substitute for traditional rehabilitation. Concerns about privacy and discomfort were low in the medical professional and public groups. Concerns about care personnel were also significantly different between the two groups. The SMPT is a promising system for providing telerehabilitation as an adjunct to traditional rehabilitation, which may result in improved outcomes compared with those achieved when using traditional rehabilitation alone.

Hemodynamics and Tissue Optical Properties in Bimodal Infarctions Induced by Middle Cerebral Artery Occlusion.

Various infarct sizes induced by middle cerebral artery occlusion (MCAO) generate inconsistent outcomes for stroke preclinical study. Monitoring cerebral hemodynamics may help to verify the outcome of MCAO. The aim of this study was to investigate the changes in brain tissue optical properties by frequency-domain near-infrared spectroscopy (FD-NIRS), and establish the relationship between cerebral hemodynamics and infarct variation in MCAO model. The rats were undergone transient MCAO using intraluminal filament. The optical properties and hemodynamics were measured by placing the FD-NIRS probes on the scalp of the head before, during, and at various time-courses after MCAO. Bimodal infarction severities were observed after the same 90-min MCAO condition. Significant decreases in concentrations of oxygenated hemoglobin ([HbO]) and total hemoglobin ([HbT]), tissue oxygenation saturation (StO2), absorption coefficient (µa) at 830 nm, and reduced scattering coefficient (µs') at both 690 and 830 nm were detected during the occlusion in the severe infarction but not the mild one. Of note, the significant increases in [HbO], [HbT], StO2, and µa at both 690 and 830 nm were found on day 3; and increases in µs' at both 690 and 830 nm were found on day 2 and day 3 after MCAO, respectively. The interhemispheric correlation coefficient (IHCC) was computed from low-frequency hemodynamic oscillation of both hemispheres. Lower IHCCs standing for interhemispheric desynchronizations were found in both mild and severe infarction during occlusion, and only in severe infarction after reperfusion. Our finding supports that sequential FD-NIRS parameters may associated with the severity of the infarction in MCAO model, and the consequent pathologies such as vascular dysfunction and brain edema. Further study is required to validate the potential use of FD-NIRS as a monitor for MCAO verification.

Motor Neuroplastic Effects of a Novel Paired Stimulation Technology in an Incomplete Spinal Cord Injury Animal Model.

Paired stimulation of the brain and spinal cord can remodel the central nervous tissue circuitry in an animal model to induce motor neuroplasticity. The effects of simultaneous stimulation vary according to the extent and severity of spinal cord injury. Therefore, our study aimed to determine the significant effects on an incomplete SCI rat brain and spinal cord through 3 min and 20 min stimulations after 4 weeks of intervention. Thirty-three Sprague Dawley rats were classified into six groups: (1) normal, (2) sham, (3) iTBS/tsDCS, (4) iTBS/ts-iTBS, (5) rTMS/tsDCS, and (6) rTMS/ts-iTBS. Paired stimulation of the brain cortex and spinal cord thoracic (T10) level was applied simultaneously for 3−20 min. The motor evoked potential (MEP) and Basso, Beattie, and Bresnahan (BBB) scores were recorded after every week of intervention for four weeks along with wheel training for 20 min. Three-minute stimulation with the iTBS/tsDCS intervention induced a significant (p < 0.050 *) increase in MEP after week 2 and week 4 treatments, while 3 min iTBS/ts-iTBS significantly improved MEP (p < 0.050 *) only after the week 3 intervention. The 20 min rTMS/ts-iTBS intervention showed a significant change only in post_5 min after week 4. The BBB score also changed significantly in all groups except for the 20 min rTMS/tsDCS intervention. iTBS/tsDCS and rTMS/ts-iTBS interventions induce neuroplasticity in an incomplete SCI animal model by significantly changing electrophysiological (MEP) and locomotion (BBB) outcomes.

Designing and Pilot Testing a Novel Transcranial Temporal Interference Stimulation Device for Neuromodulation.

Transcranial temporal interference stimulation (tTIS) has been proposed as a new neuromodulation technology for non-invasive deep-brain stimulation (DBS). However, few studies have detailed the design method of a tTIS device and provided system validation. Thus, a detailed design and validation scheme of a novel tTIS device for animal brain stimulation are presented in this study. In the proposed tTIS device, a direct digital synthesizer (DDS) was used to generate a sine wave potential of different frequencies, which was converted to an adjustable sine wave current. A current transformer was used to produce electrical isolation of different channels, which eliminated the current crosstalk between channels and greatly increased the load capacity by amplifying the output voltage. Several in vitro experiments were first conducted to validate the tTIS device. Our results indicated that the error percentages of the stimulation currents were within ±2%. Current crosstalk between channels was almost completely eliminated. Then, in vivo electric field measurement shows that the 2-pole arrangement may provide better cortical targeting than the 4-pole mode. A pilot animal experiment was conducted in which evoked motion and electromyographic activation of the contralateral forelimb were observed, which indicated that the 2-pole tTIS had successfully activated the primary motor cortex in a rat. Motor activation induced by the 2-pole tTIS demonstrated the feasibility and safety potential when applying our tTIS device for neuromodulation.

Safety of Special Waveform of Transcranial Electrical Stimulation (TES): In Vivo Assessment.

Intermittent theta burst (iTBS) powered by direct current stimulation (DCS) can safely be applied transcranially to induce neuroplasticity in the human and animal brain cortex. tDCS-iTBS is a special waveform that is used by very few studies, and its safety needs to be confirmed. Therefore, we aimed to evaluate the safety of tDCS-iTBS in an animal model after brain stimulations for 1 h and 4 weeks. Thirty-one Sprague Dawley rats were divided into two groups: (1) short-term stimulation for 1 h/session (sham, low, and high) and (2) long-term for 30 min, 3 sessions/week for 4 weeks (sham and high). The anodal stimulation applied over the primary motor cortex ranged from 2.5 to 4.5 mA/cm2. The brain biomarkers and scalp tissues were assessed using ELISA and histological analysis (H&E staining) after stimulations. The caspase-3 activity, cortical myelin basic protein (MBP) expression, and cortical interleukin (IL-6) levels increased slightly in both groups compared to sham. The serum MBP, cortical neuron-specific enolase (NSE), and serum IL-6 slightly changed from sham after stimulations. There was no obvious edema or cell necrosis seen in cortical histology after the intervention. The short- and long-term stimulations did not induce significant adverse effects on brain and scalp tissues upon assessing biomarkers and conducting histological analysis.

Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models.

Women often wear high-heeled shoes for professional or esthetic reasons. However, high-heeled shoes can cause discomfort and injury and can change the body's center of gravity when maintaining balance. This study developed an assessment system for predicting the maximal safe range for heel height by recording the plantar pressure of participants' feet by using force-sensing resistor (FSR) sensors and conducting analyses using regression models. Specifically, 100 young healthy women stood on an adjustable platform while physicians estimated the maximal safe height of high-heeled shoes. The collected FSR data combined with and without personal features were analyzed using regression models. The experimental results showed that the regression model based on the pressure data for the right foot had better predictive power than that based on data for the left foot, regardless of the module. The model with two heights had higher predictive power than that with a single height. Furthermore, adding personal features under the condition of two heights afforded the best predictive effect. These results can help wearers choose maximal safe high-heeled shoes to reduce injuries to the bones and lower limbs.

Effects of paired stimulation with specific waveforms on cortical and spinal plasticity in subjects with a chronic spinal cord injury.

BACKGROUND/PURPOSE: Paired stimulation can cause neuroplasticity in corticospinal and spinal pathways in subjects with a chronic spinal cord injury (SCI). We aimed to know the effects of different waveforms using paired stimulations with bicycling in subjects with a chronic SCI. METHODS: Recruited subjects with an SCI underwent three treatment interventions in random order for 4-20 min followed by 30 min of bicycling (control, repetitive transcranial magnetic stimulation (TMS; rTMS) at 20 Hz with transspinal direct current stimulation (tsDCS), and intermittent theta burst stimulation (iTBS) with tsDCS with a 1-week gap period. A TMS method was employed to record the resting motor threshold (RMT), the 90% values of which was used as the stimulation intensity, and the Hoffman (H)-reflex was measured by stimulating the tibial nerve in the popliteal fossa. The RMT, motor evoked potential (MEP) latency, MEP peak-to-peak amplitude, and H-reflex latency as primary variables and lower extremity motor scale (LEMS) and modified Ashworth spasticity scale (MAS) as secondary variables were analyzed before and after the interventions. RESULTS: The MEP latency, MEP amplitude, and LEMS significantly improved with the rTMS-iTBS/tsDCS or the rTMS-20 Hz/tsDCS (p < 0.050) protocols compared to the control intervention. All other outcome measures, including RMT, H-reflex latency, and MAS score showed some changes but did not fully attain a level of significance. CONCLUSION: The paired stimulation with rTMS-iTBS/tsDCS was equally effective to produce neuroplastic effect in subjects with chronic SCI compared to the conventional TMS-20 Hz/tsDCS intervention.