Machine learning-enhanced gesture recognition through impedance signal analysis.

Gesture recognition is a crucial aspect in the advancement of virtual reality, healthcare, and human-computer interaction, and requires innovative methodologies to meet the increasing demands for precision. This paper presents a novel approach that combines Impedance Signal Spectrum Analysis (ISSA) with machine learning to improve gesture recognition precision. A diverse dataset that included participants from various demographic backgrounds (five individuals) who were each executing a range of predefined gestures. The predefined gestures were designed to encompass a broad spectrum of hand movements, including intricate and subtle variations, to challenge the robustness of the proposed methodology. The machine learning model using the K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms demonstrated notable precision in performance evaluations. The individual accuracy values for each algorithm are as follows: KNN, 86%; GBM, 86%; NB, 84%; LR, 89%; RF, 87%; and SVM, 87%. These results emphasize the importance of impedance features in the refinement of gesture recognition. The adaptability of the model was confirmed under different conditions, highlighting its broad applicability.

Improvement of clinical wound microcirculation diagnosis using an object tracking-based laser speckle contrast imaging system.

Wound monitoring is crucial for effective healing, as nonhealing wounds can lead to tissue ulceration and necrosis. Evaluating wound recovery involves observing changes in angiogenesis. Laser speckle contrast imaging (LSCI) is vital for wound assessment due to its rapid imaging, high resolution, wide coverage, and noncontact properties. When using LSCI equipment, regions of interest (ROIs) must be delineated in lesion areas in images for quantitative analysis. However, patients with serious wounds cannot maintain constant postures because the affected areas are often associated with discomfort and pain. This leads to deviations between the drawn ROI and actual wound position when using LSCI for wound assessment, affecting the reliability of relevant assessments. To address these issues, we used the channel and spatial reliability tracker object tracking algorithm to develop an automatic ROI tracking function for LSCI systems. This algorithm is used to track and correct artificial movements in blood flow images, address the ROI position offset caused by the movement of the affected body part, increase the blood flow analysis accuracy, and improve the clinical applicability of LSCI systems. ROI tracking experiments were performed by simulating wounds, and the results showed that the intraclass correlation coefficient (ICC) ranged from 0.134 to 0.976. Furthermore, the object within the ROI affected tracking performance. Clinical assessments across wound types showed ICCs ranging from 0.798 to 0.917 for acute wounds and 0.628-0.849 for chronic wounds. We also discuss factors affecting tracking performance and propose strategies to enhance implementation effectiveness.

Comparative Analysis of Audio Processing Techniques on Doppler Radar Signature of Human Walking Motion Using CNN Models.

Artificial intelligence (AI) radar technology offers several advantages over other technologies, including low cost, privacy assurance, high accuracy, and environmental resilience. One challenge faced by AI radar technology is the high cost of equipment and the lack of radar datasets for deep-learning model training. Moreover, conventional radar signal processing methods have the obstacles of poor resolution or complex computation. Therefore, this paper discusses an innovative approach in the integration of radar technology and machine learning for effective surveillance systems that can surpass the aforementioned limitations. This approach is detailed into three steps: signal acquisition, signal processing, and feature-based classification. A hardware prototype of the signal acquisition circuitry was designed for a Continuous Wave (CW) K-24 GHz frequency band radar sensor. The collected radar motion data was categorized into non-human motion, human walking, and human walking without arm swing. Three signal processing techniques, namely short-time Fourier transform (STFT), mel spectrogram, and mel frequency cepstral coefficients (MFCCs), were employed. The latter two are typically used for audio processing, but in this study, they were proposed to obtain micro-Doppler spectrograms for all motion data. The obtained micro-Doppler spectrograms were then fed to a simplified 2D convolutional neural networks (CNNs) architecture for feature extraction and classification. Additionally, artificial neural networks (ANNs) and 1D CNN models were implemented for comparative analysis on various aspects. The experimental results demonstrated that the 2D CNN model trained on the MFCC feature outperformed the other two methods. The accuracy rate of the object classification models trained on micro-Doppler features was 97.93%, indicating the effectiveness of the proposed approach.

IoT-based wearable health monitoring device and its validation for potential critical and emergency applications.

The COVID-19 pandemic brought the world to a standstill, posing unprecedented challenges for healthcare systems worldwide. The overwhelming number of patients infected with the virus placed an enormous burden on healthcare providers, who struggled to cope with the sheer volume of cases. Furthermore, the lack of effective treatments or vaccines means that quarantining has become a necessary measure to slow the spread of the virus. However, quarantining places a significant burden on healthcare providers, who often lack the resources to monitor patients with mild symptoms or asymptomatic patients. In this study, we propose an Internet of Things (IoT)-based wearable health monitoring system that can remotely monitor the exact locations and physiological parameters of quarantined individuals in real time. The system utilizes a combination of highly miniaturized optoelectronic and electronic technologies, an anti-epidemic watch, a mini-computer, and a monitor terminal to provide real-time updates on physiological parameters. Body temperature, peripheral oxygen saturation (SpO2), and heart rate are recorded as the most important measurements for critical care. If these three physiological parameters are aberrant, then it could represent a life-endangering situation and/or a short period over which irreversible damage may occur. Therefore, these parameters are automatically uploaded to a cloud database for remote monitoring by healthcare providers. The monitor terminal can display real-time health data for multiple patients and provide early warning functions for medical staff. The system significantly reduces the burden on healthcare providers, as it eliminates the need for manual monitoring of patients in quarantine. Moreover, it can help healthcare providers manage the COVID-19 pandemic more effectively by identifying patients who require medical attention in real time. We have validated the system and demonstrated that it is well suited to practical application, making it a promising solution for managing future pandemics. In summary, our IoT-based wearable health monitoring system has the potential to revolutionize healthcare by providing a cost-effective, remote monitoring solution for patients in quarantine. By allowing healthcare providers to monitor patients remotely in real time, the burden on medical resources is reduced, and more efficient use of limited resources is achieved. Furthermore, the system can be easily scaled to manage future pandemics, making it an ideal solution for managing the health challenges of the future.

Bioimpedance-Measurement-Based Non-Invasive Method for In Ovo Chicken Egg Sexing.

Day-old male chick culling is one of the world's most inhumane problems in the poultry industry. Every year, seven billion male chicks are slaughtered in laying-hen hatcheries due to their higher feed exchange rate, lower management than female chicks, and higher production costs. This study describes a novel non-invasive method for determining the gender of chicken eggs. During the incubation period of fourteen days, four electrodes were attached to each egg for data collection. On the last day of incubation, a standard polymerase chain reaction (PCR)-based chicken gender determination protocol was applied to the eggs to obtain the gender information. A relationship was built between the collected data and the egg's gender, and it was discovered to have a reliable connection, indicating that the chicken egg gender can be determined by measuring the impedance data of the eggs on day 9 of incubation with the four electrodes set and using the self-normalization technique. This is a groundbreaking discovery, demonstrating that impedance spectroscopy can be used to sex chicken eggs before they hatch, relieving the poultry industry of such an ethical burden.

Therapeutic potential of nanoceria pretreatment in preventing the development of urological chronic pelvic pain syndrome: Immunomodulation via reactive oxygen species scavenging and SerpinB2 downregulation.

Urological chronic pelvic pain syndrome (UCPPS) manifests as pelvic pain with frequent urination and has a 10% prevalence rate without effective therapy. Nanoceria (cerium oxide nanoparticles [CNPs]) were synthesized in this study to achieve potential long-term pain relief, using a commonly used UCPPS mouse model with cyclophosphamide-induced cystitis. Transcriptome sequencing analysis revealed that serpin family B member 2 (SerpinB2) was the most upregulated marker in mouse bladder, and SerpinB2 was downregulated with CNP pretreatment. The transcriptome sequencing analysis results agreed with quantitative polymerase chain reaction and western blot analysis results for the expression of related mRNAs and proteins. Analysis of Gene Expression Omnibus (GEO) datasets revealed that SerpinB2 was a differentially upregulated gene in human UCPPS. In vitro SerpinB2 knockdown downregulated proinflammatory chemokine expression (chemokine receptor CXCR3 and C-X-C motif chemokine ligand 10) upon treatment with 4-hydroperoxycyclophosphamide. In conclusion, CNP pretreatment may prevent the development of UCPPS, and reactive oxygen species (ROS) scavenging and SerpinB2 downregulation may modulate the immune response in UCPPS.

Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications.

Wearable devices are being developed faster and applied more widely. Wearables have been used to monitor movement-related physiological indices, including heartbeat, movement, and other exercise metrics, for health purposes. People are also paying more attention to mental health issues, such as stress management. Wearable devices can be used to monitor emotional status and provide preliminary diagnoses and guided training functions. The nervous system responds to stress, which directly affects eye movements and sweat secretion. Therefore, the changes in brain potential, eye potential, and cortisol content in sweat could be used to interpret emotional changes, fatigue levels, and physiological and psychological stress. To better assess users, stress-sensing devices can be integrated with applications to improve cognitive function, attention, sports performance, learning ability, and stress release. These application-related wearables can be used in medical diagnosis and treatment, such as for attention-deficit hyperactivity disorder (ADHD), traumatic stress syndrome, and insomnia, thus facilitating precision medicine. However, many factors contribute to data errors and incorrect assessments, including the various wearable devices, sensor types, data reception methods, data processing accuracy and algorithms, application reliability and validity, and actual user actions. Therefore, in the future, medical platforms for wearable devices and applications should be developed, and product implementations should be evaluated clinically to confirm product accuracy and perform reliable research.

A New Biorecognition-Element-Free IDµE Sensor for the Identification and Quantification of E. coli.

The label-free biosensor has emerged as an effective tool for the purpose of early detection of causative pathogens such as Escherichia coli as a preventive measure. In this study, a biorecognition-element-free interdigitated microelectrode (IDµE) sensor is designed and developed with this in mind, with good reliability and affordability. Results show that the designed sensor can identify E. coli with good selectivity using an impedance and capacitance of 7.69 MHz. At its optimum impedance of 1.3 kHz, the IDµE sensor can reliably quantify E. coli in a range of measurement (103.2~106 cfu/mL), linearity (R2 = 0.97), sensitivity (18.15 kΩ/log (cfu/mL)), and limit of detection (103.2 cfu/mL). In summary, the IDµE sensor developed possesses high potential for industrial and clinical applications.

Intelligent Fall-Risk Assessment Based on Gait Stability and Symmetry Among Older Adults Using Tri-Axial Accelerometry.

This study aimed to use the k-nearest neighbor (kNN) algorithm, which combines gait stability and symmetry derived from a normalized cross-correlation (NCC) analysis of acceleration signals from the bilateral ankles of older adults, to assess fall risk. Fifteen non-fallers and 12 recurrent fallers without clinically significant musculoskeletal and neurological diseases participated in the study. Sex, body mass index, previous falls, and the results of the 10 m walking test (10 MWT) were recorded. The acceleration of the five gait cycles from the midsection of each 10 MWT was used to calculate the unilateral NCC coefficients for gait stability and bilateral NCC coefficients for gait symmetry, and then kNN was applied for classifying non-fallers and recurrent fallers. The duration of the 10 MWT was longer among recurrent fallers than it was among non-fallers (p < 0.05). Since the gait signals were acquired from tri-axial accelerometry, the kNN F1 scores with the x-axis components were 92% for non-fallers and 89% for recurrent fallers, and the root sum of squares (RSS) of the signals was 95% for non-fallers and 94% for recurrent fallers. The kNN classification on gait stability and symmetry revealed good accuracy in terms of distinguishing non-fallers and recurrent fallers. Specifically, it was concluded that the RSS-based NCC coefficients can serve as effective gait features to assess the risk of falls.

Noninvasive and real-time in vivo characterization of Inflammation skin. A feasibility of animal study.

BACKGROUND: Inflammatory skin diseases were the most common problem in dermatology. This study aimed to develop a circuit by using a simple method for noninvasive, objective, and real-time skin inflammation screening. MATERIALS AND METHODS: Sprague-Dawley rats were used in this study. The rats were chemically induced to suffer from skin inflammation at the back of their left-hand side while the right-hand side of their back remained untreated serving as a control. Impedance (Z) spectrum of the rat's skin was recorded. RESULTS: Two characteristic frequencies (4.5 and 48.3 kHz) were found. At the two frequencies, the impedance of inflammatory skin tissue (ZIST ) was found to be significantly (P < .05) smaller than that of normal healthy skin tissue (ZNHST ). Moreover, the ratio of the impedance measured at 4.5 kHz (Zf = 4 .5 kHz ) to the impedance measured at 48.3 kHz (Zf = 48.3 kHz ), that is, Zf = 4.5 kHz /Zf = 48.3 kHz , was capable of skin inflammation screening. It was observed that the inflammatory skin tissue (IST) had the smaller value of Zf = 4 .5 kHz /Zf = 48.3 kHz (value < 8.5) and normal healthy skin tissue (NHST) had the higher value of Zf = 4 .5 kHz /Zf = 48.3 kHz (value ≈ 10) which almost remained constant. CONCLUSION: A circuit was developed which was used for measuring the skin impedance accurately at the two characteristic frequencies for skin inflammation screening.

An Immunosensor for the Detection of ULBP2 Biomarker.

Pancreatic cancer (PC) is a global health problem that features a very high mortality rate. The UL16 binding protein 2 (ULBP2) is a new biomarker for PC detection. This study develops a simple, reliable, and inexpensive immunosensor for the detection of the ULBP2 antigen while also investigating the effects of an array configuration of connected sensors and zinc oxide (ZnO) nanoparticles on the immunosensor's sensitivity. The ULBP2 antibody was immobilized onto the screen-printed carbon electrode (SPCE) surfaces of three different sensors: a simple SPCE (ULBP2-SPCE); an SPCE array, which is a series of identical SPCE connected to each other at different arrangements of rows and columns (ULBP2-SPCE-1x2 and ULBP2-SPCE-1x3); and an SPCE combined with ZnO nanoparticles (ULBP2-ZnO/SPCE). Impedance spectrum measurements for the immunosensors to ULBP2 antigen were conducted and compared. According to the result, the array configurations (ULBP2-SPCE-1x2 and ULBP2-SPCE-1x3) show an improvement of sensitivity compared to the ULBP2-SPCE alone, but the improvement is not as significant as that of the ULBP2-ZnO/SPCE configuration (ULBP2-ZnO/SPCE > ULBP2-SPCE: 18 times larger). The ULBP2-ZnO/SPCE immunosensor has a low limit of detection (1 pg/mL) and a high sensitivity (332.2 Ω/Log(pg/mL)), excellent linearity (R2 = 0.98), good repeatability (coefficients of variation = 5.03%), and is stable in long-term storage (retaining 95% activity after 28 days storage). In an array configuration, the immunosensor has an increased signal-to-noise ratio (ULBP2-SPCE-1x3 > ULBP2-SPCE: 1.5-fold) and sensitivity (ULBP2-SPCE-1x3 > ULBP2-SPCE: 2.6-fold). In conclusion, either the modification with ZnO nanoparticles onto the sensor or the use of an array configuration of sensors can enhance the immunosensor's sensitivity. In this study, the best immunosensor for detecting ULBP2 antigens is the ULBP2-ZnO/SPCE immunosensor.

A Reusable Mask for Coronavirus Disease 2019 (COVID-19).

The outbreak of Novel Coronavirus is causing an intensely feared globally. World Health Organization has even declared that it is a global health emergency. The simplest method to limit the spread of this new virus and for people to protect themselves as well as the others is to wear a mask in crowded places. The sudden increase demand on face mask has caused manufacturers the inability to not provide enough products in a short time and the situation properly will stay the same for a period of time. In this article, we aim to give an idea on how to save the number of face masks used but still provides the same protective values using a Cardiopulmonary resuscitation (CPR) mask and a common surgical facemask.

Fatigue changes neck muscle control and deteriorates postural stability during arm movement perturbations in patients with chronic neck pain.

BACKGROUND CONTEXT: Multisensory afferent inputs to the cervical spine affect the generation of neck muscle control. Chronic neck pain (CNP) and muscle fatigue are factors that disturb somatosensory function. Whether they affect postural control under self-initiated perturbation in daily activities is still unclear. PURPOSE: To investigate the effect of CNP and neck flexor muscle fatigue on muscle control strategy and postural control in young patients performing voluntary shoulder flexion movements. STUDY DESIGN: Cross-sectional case-control study. PATIENT SAMPLE: Twenty-five patients with CNP and 25 age-matched asymptomatic controls. OUTCOME MEASURES: The postural sway, muscle onset time, and activation level of the erector spinae, rectus abdominal, semispinalis capitis (SSC), and sternocleidomastoid (SCM) muscles were recorded and analyzed using two-way ANOVA to evaluate the interaction of CNP and muscle fatigue on standing balance and muscle control upon self-initiated perturbations. METHODS: All participants were instructed to perform shoulder flexion movements in the standing position before and after a neck flexor muscle fatigue exercise under either the eyes-open or eyes-closed condition. RESULTS: The CNP group exhibited significantly larger body sway, greater neck muscle activation (SCM and SSC), and longer onset time of neck flexor muscle (SCM) compared with the control group. The CNP group also demonstrated a trend of greater postural sway and shorter muscle onset under the eyes-closed condition than those under the eyes-open condition. After muscle fatigue, the CNP group further exhibited (1) greater body sway during the eyes-open condition but decreased body sway during the eyes-closed condition, (2) higher activation of the neck flexor (SCM) and lower activation of the trunk extensor (erector spinae), and (3) early onset of the neck muscles (SCM and SSC). CONCLUSIONS: CNP causes poor postural control and altered neck muscle control patterns. The addition of neck flexor muscle fatigue further decreases balance stability and provokes a protective neck muscle control strategy during the shoulder flexion movement. Those findings facilitate the understanding of the strategies adopted by patients and suggest that neck endurance training programs may be beneficial to improve whole postural control in patients with CNP.

Wireless Patrol Sign-In System with Mental Fatigue Detection.

Current sign-in methods of patrolling security guards mainly comprise signature, image identification, and fingerprint identification; notably, none of these methods indicate the physical and mental conditions of such guards. In particular, when patrolling security guards perform their duties consecutively for a long period of time, adequate attention should be directed toward their levels of mental fatigue. When a handwriting sign-in system is adopted, security guards may not record their sign-in time accurately, or they may fake signatures during long shifts. In addition, image identification systems cannot comprehensively reflect the physical and mental statuses of on-duty security guards, particularly their levels of fatigue. Monitor fatigue in patrolling security guards is important to avoid burnout and stress in the workplace. Therefore, in this study, a patrolling sign-in system that integrates physiological signals and images was designed. A thermometer, hand dynamometer, and electromyography sensor were combined to measure physiological signals. Results showed that hand grip strength and the median frequency of electromyography signals gradually reduced when muscle fatigue occurred. The system determined whether a security guard had signed in punctually and whether this person should stay on duty. Overall, this system was verified to operate effectively, and it is therefore applicable for monitoring the sign-in of patrolling security guards who work long shifts. This case series study proposed a conceptual prototype of the system; large-scale testing should be performed in subsequent research.

A Mechanical Sensor Designed for Dynamic Joint Angle Measurement.

Background. The measurement of the functional range of motion (FROM) of lower limb joints is an essential parameter for gait analysis especially in evaluating rehabilitation programs. Aim. To develop a simple, reliable, and affordable mechanical goniometer (MGR) for gait analysis, with six-degree freedom to dynamically assess lower limb joint angles. Design. Randomized control trials, in which a new MGR was developed for the measurements of FROM of lower limb joints. Setting. Reliability of the designed MGR was evaluated and validated by a motion analysis system (MAS). Population. Thirty healthy subjects participated in this study. Methods. Reliability and validity of the new MGR were tested by intraclass correlation coefficient (ICC), Bland-Altman plots, and linear correlation analysis. Results. The MGR has good inter- and intrarater reliability and validity with ICC ≥ 0.93 (for both). Moreover, measurements made by MGR and MAS were comparable and repeatable with each other, as confirmed by Bland-Altman plots. Furthermore, a very high degree of linear correlation (R ≥ 0.92 for all joint angle measurements) was found between the lower limb joint angles measured by MGR and MAS. Conclusion. A simple, reliable, and affordable MGR has been designed and developed to aid clinical assessment and treatment evaluation of gait disorders.

The Evaluation of Physical Stillness with Wearable Chest and Arm Accelerometer during Chan Ding Practice.

Chan Ding training is beneficial to health and emotional wellbeing. More and more people have taken up this practice over the past few years. A major training method of Chan Ding is to focus on the ten Mailuns, i.e., energy points, and to maintain physical stillness. In this article, wireless wearable accelerometers were used to detect physical stillness, and the created physical stillness index (PSI) was also shown. Ninety college students participated in this study. Primarily, accelerometers used on the arms and chest were examined. The results showed that the PSI values on the arms were higher than that of the chest, when participants moved their bodies in three different ways, left-right, anterior-posterior, and hand, movements with natural breathing. Then, they were divided into three groups to practice Chan Ding for approximately thirty minutes. Participants without any Chan Ding experience were in Group I. Participants with one year of Chan Ding experience were in Group II, and participants with over three year of experience were in Group III. The Chinese Happiness Inventory (CHI) was also conducted. Results showed that the PSI of the three groups measured during 20-30 min were 0.123 ± 0.155, 0.012 ± 0.013, and 0.001 ± 0.0003, respectively (p < 0.001 ***). The averaged CHI scores of the three groups were 10.13, 17.17, and 25.53, respectively (p < 0.001 ***). Correlation coefficients between PSI and CHI of the three groups were -0.440, -0.369, and -0.537, respectively (p < 0.01 **). PSI value and the wearable accelerometer that are presently available on the market could be used to evaluate the quality of the physical stillness of the participants during Chan Ding practice.

Liver Cancer Detection by a Simple, Inexpensive and Effective Immunosensor with Zinc Oxide Nanoparticles.

Regular monitoring of blood α-fetoprotein (AFP) and/or carcino-embryonic antigen (CEA) levels is important for the routine screening of liver cancer. However, AFP and CEA have a much lower specificity than des-γ-carboxyprothrombin (DCP) to detect liver cancer. Therefore, the study reported here was designed, to develop a screen-printed DCP immunosensor incorporating zinc oxide nanoparticles, for accurate determination of DCP. The designed immunosensor shows low detection limits for the detection of DCP: 0.440 ng/mL (based on impedance measurement), 0.081 ng/mL (based on real part of impedance measurement) and 0.078 ng/mL (based on imaginary part of impedance measurement), within the range of 3.125 ng/mL to 2000 ng/mL. In addition, there was little interference to DCP determination by molecules such as Na⁺, K⁺, Ca(2+), Cl(-), glucose, urea, and uric acid. It is therefore concluded that the DCP immunosensor developed and reported here is simple, inexpensive and effective, and shows promise in the rapid screening of early-stage liver cancer at home with a point-of-care approach.

A new approach for noninvasive transdermal determination of blood uric acid levels.

The aims of this study were to investigate the most effective combination of physical forces from laser, electroporation, and reverse iontophoresis for noninvasive transdermal extraction of uric acid, and to develop a highly sensitive uric acid biosensor (UAB) for quantifying the uric acid extracted. It is believed that the combination of these physical forces has additional benefits for extraction of molecules other than uric acid from human skin. A diffusion cell with porcine skin was used to investigate the most effective combination of these physical forces. UABs coated with ZnO2 nanoparticles and constructed in an array configuration were developed in this study. The results showed that a combination of laser (0.7 W), electroporation (100 V/cm(2)), and reverse iontophoresis (0.5 mA/cm(2)) was the most effective and significantly enhanced transdermal extraction of uric acid. A custom-designed UAB coated with ZnO2 nanoparticles and constructed in a 1×3 array configuration (UAB-1×3-ZnO2) demonstrated enough sensitivity (9.4 µA/mM) for quantifying uric acid extracted by the combined physical forces of laser, electroporation, and RI. A good linear relationship (R(2)=0.894) was demonstrated to exist between the concentration of uric acid (0.2-0.8 mM) inside the diffusion cell and the current response of the UAB-1×3-ZnO2. In conclusion, a new approach to noninvasive transdermal extraction and quantification of uric acid has been established.

Characterization of the muscle electrical properties in low back pain patients by electrical impedance myography.

OBJECTIVES: This study aims to investigate the electrical properties of lumbar paraspinal muscles (LPM) of patients with acute lower back pain (LBP) and to study a new approach, namely Electrical Impedance Myography (EIM), for reliable, low-cost, non-invasive, and real-time assessment of muscle-strained acute LBP. DESIGN: Patients with muscle-strained acute LBP (n = 30) are compared to a healthy reference group (n = 30). Electrical properties of LPM are studied. BACKGROUND: EIM is a novel technique under development for the assessment of neuromuscular disease. Therefore, it is speculated that EIM can be employed for the assessment of muscle-strained acute LBP. METHODS: Surface electrodes, in 2-electrode configurations, was used to measure the electrical properties of patient's and healthy subject's LPM at six different frequencies (0.02, 25.02, 50.02, 1000.02, 3000.02, and 5000.02 kHz), with the amplitude of the applied voltage limited to 200 mV. Parameters of impedance (Z), extracellular resistance (Re), intracellular resistance (Ri), and the ratio of extracellular resistance to intracellular resistance (Re/Ri) of LBP patient's and healthy subject's LPM were assessed to see if significant difference in values obtained in muscle-strained acute LBP patients existed. RESULTS: Intraclass correlation coefficient (ICC) showed that all measurements (ICC>0.96 for all studying parameters: Z, Re, Ri, and Re/Ri) had good reliability and validity. Significant differences were found on Z between LBP patient's and healthy subject's LPM at all studying frequencies, with p<0.05 for all frequencies. It was also found that Re (p<0.05) and Re/Ri (p<0.05) of LBP patient's LPM was significant smaller than that of healthy subjects while Ri (p<0.05) of LBP patient's LPM was significant greater than that of healthy subjects. No statistical significant difference was found between the left and right LPM of LBP patients and healthy subjects on the four studying parameters. CONCLUSION: EIM is a promising technique for assessing muscle-strained acute LBP.

A wireless accelerometer-based body posture stability detection system and its application for meditation practitioners.

The practice of meditation has become an interesting research issue in recent decades. Meditation is known to be beneficial for health improvement and illness reduction and many studies on meditation have been made, from both the physiological and psychological points of view. It is a fundamental requirement of meditation practice to be able to sit without body motion. In this study, a novel body motion monitoring and estimation system has been developed. A wireless tri-axis accelerometer is used to measure body motion. Both a mean and maximum motion index is derived from the square summation of three axes. Two experiments were conducted in this study. The first experiment was to investigate the motion index baseline among three leg-crossing postures. The second experiment was to observe posture dynamics for thirty minute's meditation. Twenty-six subjects participated in the experiments. In one experiment, thirteen subjects were recruited from an experienced meditation group (meditation experience > 3 years); and the other thirteen subjects were beginners (meditation experience < 1 years). There was a significant posture stability difference between both groups in terms of either mean or maximum parameters (p < 0.05), according to the results of the experiment. Results from another experiment showed that the motion index is different for various postures, such as full-lotus < half-lotus < non-lotus.