Exploring the mechanism underlying the antifungal activity of chitosan-based ZnO, CuO, and SiO2 nanocomposites as nanopesticides against Fusarium solani and Alternaria solani.

Chitosan-based nanocomposites (CS NCs) are gaining considerable attention as multifaceted antifungal agents. This study investigated the antifungal activity of NCs against two phytopathogenic strains: Fusarium solani (F. solani) and Alternaria solani (A. solani). Moreover, it sheds light on their underlying mechanisms of action. The NCs, CS-ZnO, CS-CuO, and CS-SiO2, were characterized using advanced methods. Dynamic and electrophoretic light scattering techniques revealed their size range (60-170 nm) and cationic nature, as indicated by the positive zeta potential values (from +16 to +22 mV). Transmission electron microscopy revealed the morphology of the NCs as agglomerates formed between the chitosan and oxide components. X-ray diffraction patterns confirmed crystalline structures with specific peaks indicating their constituents. Antifungal assessments using the agar diffusion technique demonstrated significant inhibitory effects of the NCs on both fungal strains (1.5 to 4-fold), surpassing the performance of the positive control, nystatin. Notably, the NCs exhibited superior antifungal potency, with CS-ZnO NCs being the most effective. A. solani was the most sensitive strain to the studied agents. Furthermore, the tested NCs induced oxidative stress in fungal cells, which elevated stress biomarker levels, such as superoxide dismutase (SOD) activity and protein carbonyl content (PCC), 2.5 and 6-fold for the most active CS-CuO in F. solani respectively. Additionally, they triggered membrane lipid peroxidation up to 3-fold higher compared to control, a process that potentially compromises membrane integrity. Laurdan fluorescence spectroscopy highlighted alterations in the molecular organization of fungal cell membranes induced by the NCs. CS-CuO NCs induced a membrane rigidifying effect, while CS-SiO2 and CS-ZnO could rigidify membranes in A. solani and fluidize them in F. solani. In summary, this study provides an in-depth understanding of the interactions of CS-based NCs with two fungal strains, showing their antifungal activity and offering insights into their mechanisms of action. These findings emphasize the potential of these NCs as effective and versatile antifungal agents.

Modification of micro-crystalline graphite and carbon black by acetone, toluene, and phenol.

The chemical interactions of two types of graphite and two types of carbon black (CB) with acetone, toluene, and phenol were studied in order to evaluate the influence of chemical treatment on the structure and morphology of the carbon phases. The experimental treatment of carbon phases was carried out at room temperature for 1 hour. The chemical and phase composition were studied by x-ray photoelectron (XP) and Raman spectroscopies, while the morphology and structure were determined by powder x-ray diffraction, as well as transmission electron microscopy techniques. To shed light on the most probable explanation of the observed results, we performed simulations and calculations of the binding energies of acetone, toluene, and phenol with model carbon phases: a perfect graphene sheet and a defective graphene sheet containing various structural defects (vacancies as well as zigzag and armchair edges). Simulations show that all non-covalent and most covalent coupling reactions are exothermic, with acetone coupling having the higher calorimetric effect. Based on the results of the simulations and the XP spectroscopy measurements, the probable reactions taking place during the respective treatments are outlined. The conducted studies (both theoretical and experimental) show that the treatment of graphite powders and CB with acetone, toluene, or phenol can be used as a preliminary stage of their modification and/or functionalization, including their conversion into graphene-like (defective graphene, reduced graphene oxide, and/or graphene oxide) phases. For example, the treatment of SPHERON 5000 with acetone significantly facilitates their subsequent modification with laser radiation to graphene-like phases.

A Soft Wearable and Fully-Textile Piezoresistive Sensor for Plantar Pressure Capturing.

The trends of wearable health monitoring systems have led to growing demands for gait-capturing devices. However, comfortability and durability under repeated stress are still challenging to achieve in existing sensor-enabled footwear. Herein, a flexible textile piezoresistive sensor (TPRS) consisting of a reduced graphene oxide (rGO)-cotton) fabric electrode and an Ag fabric circuit electrode is proposed. Based on the mechanical and electrical properties of the two fabric electrodes, the TPRS exhibits superior sensing performance, with a high sensitivity of 3.96 kPa-1 in the lower pressure range of 0-36 kPa, wide force range (0-100 kPa), fast response time (170 ms), remarkable durability stability (1000 cycles) and detection ability in different pressures ranges. For the prac-tical application of capturing plantar pressure, six TPRSs were mounted on a flexible printed circuit board and integrated into an insole. The dynamic plantar pressure distribution during walking was derived in the form of pressure maps. The proposed fully-textile piezoresistive sensor is a strong candidate for next-generation plantar pressure wearable monitoring devices.

Gait Rhythm Dynamics for Neuro-Degenerative Disease Classification via Persistence Landscape- Based Topological Representation.

Neuro-degenerative disease is a common progressive nervous system disorder that leads to serious clinical consequences. Gait rhythm dynamics analysis is essential for evaluating clinical states and improving quality of life for neuro-degenerative patients. The magnitude of stride-to-stride fluctuations and corresponding changes over time-gait dynamics-reflects the physiology of gait, in quantifying the pathologic alterations in the locomotor control system of health subjects and patients with neuro-degenerative diseases. Motivated by algebra topology theory, a topological data analysis-inspired nonlinear framework was adopted in the study of the gait dynamics. Meanwhile, the topological representation-persistence landscapes were used as input of classifiers in order to distinguish different neuro-degenerative disease type from healthy. In this work, stride-to-stride time series from healthy control (HC) subjects are compared with the gait dynamics from patients with amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Parkinson's disease (PD). The obtained results show that the proposed methodology discriminates healthy subjects from subjects with other neuro-degenerative diseases with relatively high accuracy. In summary, our study is the first attempt to provide a topological representation-based method into the disease classification with gait rhythms measured from the stride intervals to visualize gait dynamics and classify neuro-degenerative diseases. The proposed method could be potentially used in earlier interventions and state monitoring.

Correction to: An explorative investigation of functional differences in plantar center of pressure of four foot types using sample entropy method.

In the original publication of the article the affiliation of Kamen Ivanov was inaccurate. The correct affiliation of Kamen Ivanov is given below.

The EEG Analysis of Actual Left/Right Lateral Bending Movements in Patient of Lumbar Disc Herniation.

The purpose of this study was to investigate surface EEG of two groups (lumbar disc herniation (LDH) patients and healthy controls (HC)) when they performed real smooth movements (left/right lateral bending) with the maximum voluntary movement without pain. Thirty female LDH patients and thirty healthy controls volunteered to participate in the experiment. We also tested a healthy participant's motion imaginery (MI) of left/right lateral bending movement for over 200 times. We used Daubechies 4 (db4) wavelet to decompose EEG signal and we extracted δ, θ, α and ß rhythms of the EEG signal. Wavelet entropy and sample entropy (SampEn) of four frequency bands were calculated. The results showed that there was significant difference of wavelet entropy EEG in T7, O2, and AF4 channels between the LDH and the healthy group when they did real left lateral bending. The topographic map also showed that SampEn value of four rhythms of the MI right lateral bending were significantly less than the values of the MI left lateral motion in healthy participant. Classification and Regression Trees (CART), Logistic regression (LR), Support Vector Machine (SVM), K-nearest neighbors (KNN), and the Linear discrimination analysis (LDA) classifiers showed averaged accuracies more than 96% for MI of left/right lateral bending.

Learning-based Parameter Estimation for Hysteresis Modeling in Robotic Catheterization.

In the last half decade, nearly 31% of annual global deaths are linked to cardiovascular diseases. Thus, robotic catheterizations are recently proposed for interventions of conditions such as aneurism or atherosclerosis formed along vascular paths leading to the heart. However, existence of mild to strong hysteresis while navigating unactuated catheters with the current robotic systems inhibits autonomous control for vascular surgery. Thus, immersion of surgeons remains high with most of their time spent on steering the catheter in-and- out of the vessels. In this study, an autoregressive nonlinear neural network model is adapted for parameterization of vital causal factors of hysteresis during robotic catheterization. Crucial for autonomous control, hysteretic behaviors of endovascular tool are modeled while suitable values are estimated and analyzed for five contributory factors. The network model is validated with hysteresis data we obtained from a two degree-of-freedom robotic system and an unactuated catheter. Result validation shows accurate description of the hysteresis profile recorded during catheterization trials with a vascular phantom model.

Signal Quality and Electrode-Skin Impedance Evaluation in the Context of Wearable Electroencephalographic Systems.

Recent advancement in technology has brought about increase in the application areas of wearable electroencephalographic devices. In that, new types of electrodes take place, and particular attention is needed to ensure the required quality of obtained signals. In this study, we evaluate electrode-skin impedance and signal quality for several kinds of electrodes when used in conditions typical for wearable devices. Results suggest that active dry electrode coated with gold alloy is superior while it was challenging to obtain appropriate signal quality when using passive dry electrodes. We also demonstrate electrode-skin impedance measurement using the analog frontend ADS1299, which is suitable for implementation in wearable devices.

Non-iterative geometric approach for inverse kinematics of redundant lead-module in a radiosurgical snake-like robot.

BACKGROUND: Snake-like robot is an emerging form of serial-link manipulator with the morphologic design of biological snakes. The redundant robot can be used to assist medical experts in accessing internal organs with minimal or no invasion. Several snake-like robotic designs have been proposed for minimal invasive surgery, however, the few that were developed are yet to be fully explored for clinical procedures. This is due to lack of capability for full-fledged spatial navigation. In rare cases where such snake-like designs are spatially flexible, there exists no inverse kinematics (IK) solution with both precise control and fast response. METHODS: In this study, we proposed a non-iterative geometric method for solving IK of lead-module of a snake-like robot designed for therapy or ablation of abdominal tumors. The proposed method is aimed at providing accurate and fast IK solution for given target points in the robot's workspace. n-1 virtual points (VPs) were geometrically computed and set as coordinates of intermediary joints in an n-link module. Suitable joint angles that can place the end-effector at given target points were then computed by vectorizing coordinates of the VPs, in addition to coordinates of the base point, target point, and tip of the first link in its default pose. The proposed method is applied to solve IK of two-link and redundant four-link modules. RESULTS: Both two-link and four-link modules were simulated with Robotics Toolbox in Matlab 8.3 (R2014a). Implementation result shows that the proposed method can solve IK of the spatially flexible robot with minimal error values. Furthermore, analyses of results from both modules show that the geometric method can reach 99.21 and 88.61% of points in their workspaces, respectively, with an error threshold of 1 mm. The proposed method is non-iterative and has a maximum execution time of 0.009 s. CONCLUSIONS: This paper focuses on solving IK problem of a spatially flexible robot which is part of a developmental project for abdominal surgery through minimal invasion or natural orifices. The study showed that the proposed geometric method can resolve IK of the snake-like robot with negligible error offset. Evaluation against well-known methods shows that the proposed method can reach several points in the robot's workspace with high accuracy and shorter computational time, simultaneously.

A Novel Technique for Fetal ECG Extraction Using Single-Channel Abdominal Recording.

Non-invasive fetal electrocardiograms (FECGs) are an alternative method to standard means of fetal monitoring which permit long-term continual monitoring. However, in abdominal recording, the FECG amplitude is weak in the temporal domain and overlaps with the maternal electrocardiogram (MECG) in the spectral domain. Research in the area of non-invasive separations of FECG from abdominal electrocardiograms (AECGs) is in its infancy and several studies are currently focusing on this area. An adaptive noise canceller (ANC) is commonly used for cancelling interference in cases where the reference signal only correlates with an interference signal, and not with a signal of interest. However, results from some existing studies suggest that propagation of electrocardiogram (ECG) signals from the maternal heart to the abdomen is nonlinear, hence the adaptive filter approach may fail if the thoracic and abdominal MECG lack strict waveform similarity. In this study, singular value decomposition (SVD) and smooth window (SW) techniques are combined to build a reference signal in an ANC. This is to avoid the limitation that thoracic MECGs recorded separately must be similar to abdominal MECGs in waveform. Validation of the proposed method with r01 and r07 signals from a public dataset, and a self-recorded private dataset showed that the proposed method achieved F1 scores of 99.61%, 99.28% and 98.58%, respectively for the detection of fetal QRS. Compared with four other single-channel methods, the proposed method also achieved higher accuracy values of 99.22%, 98.57% and 97.21%, respectively. The findings from this study suggest that the proposed method could potentially aid accurate extraction of FECG from MECG recordings in both clinical and commercial applications.

An explorative investigation of functional differences in plantar center of pressure of four foot types using sample entropy method.

In the study of biomechanics of different foot types, temporal or spatial parameters derived from plantar pressure are often used. However, there is no comparative study of complexity and regularity of the center of pressure (CoP) during the stance phase among pes valgus, pes cavus, hallux valgus and normal foot. We aim to analyze whether CoP sample entropy characteristics differ among these four foot types. In our experiment participated 40 subjects with normal feet, 40 with pes cavus, 19 with pes valgus and 36 with hallux valgus. A Footscan® system was used to collect CoP data. We used sample entropy to quantify several parameters of the investigated four foot types. These are the displacement in medial-lateral (M/L) and anterior-posterior (A/P) directions, as well as the vertical ground reaction force of CoP during the stance phase. To fully examine the potential of the sample entropy method for quantification of CoP components, we provide results for two cases: calculating the sample entropy of normalized CoP components, as well as calculating it using the raw data of CoP components. We also explored what are the optimal values of parameters m (the matching length) and r (the tolerance range) when calculating the sample entropy of CoP data obtained during the stance phases. According to statistical results, some factors significantly influenced the sample entropy of CoP components. The sample entropies of non-normalized A/P values for the left foot, as well as for the right foot, were different between the normal foot and pes valgus, and between the normal foot and hallux valgus. The sample entropy of normalized M/L displacement of the right foot was different between the normal foot and pes cavus. The measured variable for A/P and M/L displacements could serve for the study of foot function.

Toward a Smartphone Application for Estimation of Pulse Transit Time.

Pulse transit time (PTT) is an important physiological parameter that directly correlates with the elasticity and compliance of vascular walls and variations in blood pressure. This paper presents a PTT estimation method based on photoplethysmographic imaging (PPGi). The method utilizes two opposing cameras for simultaneous acquisition of PPGi waveform signals from the index fingertip and the forehead temple. An algorithm for the detection of maxima and minima in PPGi signals was developed, which includes technology for interpolation of the real positions of these points. We compared our PTT measurements with those obtained from the current methodological standards. Statistical results indicate that the PTT measured by our proposed method exhibits a good correlation with the established method. The proposed method is especially suitable for implementation in dual-camera-smartphones, which could facilitate PTT measurement among populations affected by cardiac complications.

A novel method based on two cameras for accurate estimation of arterial oxygen saturation.

BACKGROUND: Photoplethysmographic imaging (PPGi) that is based on camera allows acquiring photoplethysmogram and measuring physiological parameters such as pulse rate, respiration rate and perfusion level. It has also shown potential for estimation of arterial oxygen saturation (SaO2). However, there are some technical limitations such as optical shunting, different camera sensitivity to different light spectra, different AC-to-DC ratios (the peak-to-peak amplitude to baseline ratio) of the PPGi signal for different portions of the sensor surface area, the low sampling rate and the inconsistency of contact force between the fingertip and camera lens. METHODS: In this paper, we take full account of the above-mentioned design challenges and present an accurate SaO2 estimation method based on two cameras. The hardware system we used consisted of an FPGA development board (XC6SLX150T-3FGG676 from Xilinx), with connected to it two commercial cameras and an SD card. The two cameras were placed back to back, one camera acquired PPGi signal from the right index fingertip under 660 nm light illumination while the other camera acquired PPGi signal from the thumb fingertip using an 800 nm light illumination. The both PPGi signals were captured simultaneously, recorded in a text file on the SD card and processed offline using MATLAB®. The calculation of SaO2 was based on the principle of pulse oximetry. The AC-to-DC ratio was acquired by the ratio of powers of AC and DC components of the PPGi signal in the time-frequency domain using the smoothed pseudo Wigner-Ville distribution. The calibration curve required for SaO2 measurement was obtained by linear regression analysis. RESULTS: The results of our estimation method from 12 subjects showed a high correlation and accuracy with those of conventional pulse oximetry for the range from 90 to 100%. CONCLUSIONS: Our method is suitable for mobile applications implemented in smartphones, which could allow SaO2 measurement in a pervasive environment.

Estimation and visualization of longitudinal muscle motion using ultrasonography: a feasibility study.

Ultrasonography is a convenient and widely used technique to look into the longitudinal muscle motion as it is radiation-free and real-time. The motion of localized parts of the muscle, disclosed by ultrasonography, spatially reflects contraction activities of the corresponding muscles. However, little attention was paid to the estimation of longitudinal muscle motion, especially towards estimation of dense deformation field at different depths under the skin. Yet fewer studies on the visualization of such muscle motion or further clinical applications were reported in the literature. A primal-dual algorithm was used to estimate the motion of gastrocnemius muscle (GM) in longitudinal direction in this study. To provide insights into the rules of longitudinal muscle motion, we proposed a novel framework including motion estimation, visualization and quantitative analysis to interpret synchronous activities of collaborating muscles with spatial details. The proposed methods were evaluated on ultrasound image sequences, captured at a rate of 25 frames per second from eight healthy subjects. In order to estimate and visualize the GM motion in longitudinal direction, each subject was asked to perform isometric plantar flexion twice. Preliminary results show that the proposed visualization methods provide both spatial and temporal details and they are helpful to study muscle contractions. One of the proposed quantitative measures was also tested on a patient with unilateral limb dysfunction caused by cerebral infarction. The measure revealed distinct patterns between the normal and the dysfunctional lower limb. The proposed framework and its associated quantitative measures could potentially be used to complement electromyography (EMG) and torque signals in functional assessment of skeletal muscles.

Exploration and comparison of the pre-impact lead time of active and passive falls based on inertial sensors.

Research on falls in elderly people has a great social significance because of the rapidly growing of the aging population. The pre-impact lead time of fall (PLT) is an important part of the human fall theory. PLT is the longest time for a person who is going to fall to take action in order to prevent the fall or to reduce bodily injuries from the fall impact. However, there is no clear definition of PLT so far. There is also no comparative study for active and passive falls. In this study, we proposed a theoretical definition of the PLT, based on a new method of fall event division. We also compared the differences of PLT and the related angles between active and passive falls. Eight healthy adult subjects were arranged to perform three kinds of activities of daily living (sitting, walking and lying), and two kinds fall activities (active and passive) in three directions (forward, backward and lateral fall). Nine inertial sensor modules were used to measure the body segmental kinematic characteristics of each subject in our experimental activities. In this paper, a fall event was suggested to divide into three or four phases and then the critical phase could be divided into three periods (pre-impact, impact, and post-impact). Two fall models were developed for active and passive falls using acceleration data. The average value of PLT for active falls is about 514 ± 112 ms and it is smaller than the value for passive falls, which is 731 ± 104 ms. The longest PLTs were measured on the chest or waist instead of other locations, such as the thigh and shank. The PLTs of the three kinds of fall activities were slightly different, but there was a significant difference between two fall modes. The PLT showed the correlation to the body angle at the start of PLT, but it was uncorrelated at the end of PLT. The angles at the start of PLT had slight variations (<10 degrees) from the steady standing state except in passive forward falls (max 16 degrees) due to the self-control. The landing angles were significantly different in the both fall modes in all the three directions of fall, indicating the state of the trunk was uncertain when the hip contacted the ground. It can be concluded that it is feasible to prevent falls by using an early pre-impact fall alarm device; the present study provides important reference for development of pre-impact fall alarm devices.

Sample entropy characteristics of movement for four foot types based on plantar centre of pressure during stance phase.

BACKGROUND: Motion characteristics of CoP (Centre of Pressure, the point of application of the resultant ground reaction force acting on the plate) are useful for foot type characteristics detection. To date, only few studies have investigated the nonlinear characteristics of CoP velocity and acceleration during the stance phase. The aim of this study is to investigate whether CoP regularity is different among four foot types (normal foot, pes valgus, hallux valgus and pes cavus); this might be useful for classification and diagnosis of foot injuries and diseases. To meet this goal, sample entropy, a measure of time-series regularity, was used to quantify the CoP regularity of four foot types. METHODS: One hundred and sixty five subjects that had the same foot type bilaterally (48 subjects with healthy feet, 22 with pes valgus, 47 with hallux valgus, and 48 with pes cavus) were recruited for this study. A Footscan® system was used to collect CoP data when each subject walked at normal and steady speed. The velocity and acceleration in medial-lateral (ML) and anterior-posterior (AP) directions, and resultant velocity and acceleration were derived from CoP. The sample entropy is the negative natural logarithm of the conditional probability that a subseries of length m that matches pointwise within a tolerance r also matches at the next point. This was used to quantify variables of CoP velocity and acceleration of four foot types. The parameters r (the tolerance) and m (the matching length) for sample entropy calculation have been determined by an optimal method. RESULTS: It has been found that in order to analyze all CoP parameters of velocity and acceleration during the stance phase of walking gait, for each variable there is a different optimal r value. On the contrary, the value m=4 is optimal for all variables.Sample entropies of both velocity and acceleration in AP direction were highly correlated with their corresponding resultant variables for r>0.91. The sample entropy of the velocity in AP direction was moderately correlated with the one of the acceleration in the same direction (r≥0.673), as well as with the resultant acceleration (r≥0.660). The sample entropy of resultant velocity was moderately correlated with the one of the acceleration in AP direction, as well as with the resultant acceleration (for the both r≥0.689). Moderate correlations were found between variables for the left foot and their corresponding variables for the right foot.Sample entropies of AP velocity, resultant velocity, AP acceleration, and resultant acceleration of the right foot as well as AP velocity and resultant velocity of the left foot were, respectively, significantly different among the four foot types. CONCLUSIONS: It can be concluded that the sample entropy of AP velocity (or the resultant velocity) of the left foot, ML velocity, resultant velocity, ML acceleration and resultant acceleration could serve for evaluation of foot types or selection of appropriate footwear.

Pre-processing for muscle motion analysis: adaptive guided image filtering for speckle reduction of ultrasound images.

Skeletal muscle is an important tissue of human body, and its contractions control and regulate body motions. Muscle contraction results in morphological changes of the related muscles. Ultrasound imaging is an effective tool for studying muscle architectures and monitoring the morphological changes of muscles. The latter process can be realized with a motion estimation algorithm. However, ultrasound images are usually corrupted by speckle noises and performance of motion estimation methods can be significantly affected by the noises. To get a better performance in motion analysis, in this paper, as a pre-processing step, an adaptive filter named adaptive guided image filtering (AGF) is suggested to reduce speckle noises. We first transformed the multiplicative noise model into an additive one by taking the logarithm of the original speckled data, then performed AGF to obtain the filtered image, and finally took the tackled image back into exponent. Experimental results showed that AGF had a better performance in terms of noise attenuation and edge preservation compared with other standard filters. In quantitative results, the filtered images also had the highest Peak-Signal-to-Noise Ratio (PSNR) using AGF. It's believed that AGF is a good choice for the pre-processing stage of muscle motion analysis.

Balance and knee extensibility evaluation of hemiplegic gait using an inertial body sensor network.

BACKGROUND: Most hemiplegic patients have difficulties in their balance and posture control while walking because of the asymmetrical posture and the abnormal body balance. The assessment of rehabilitation of hemiplegic gait is usually made by doctors using clinical scale, but it is difficult and could not be used frequently. It is therefore needed to quantitatively analyze the characteristics of hemiplegic gait. Thus the assessment would be simple, and real-time evaluation of rehabilitation could be carried out. METHODS: Twenty subjects (ten hemiplegic patients, ten normal subjects) were recruited. The subjects walked straight for five meters at their self-selected comfortable speed towards a target line on the floor.Xsens MTx motion trackers were used for acquiring gestures of body segments to estimate knee joint angles and identify gait cycles. A practical method for data acquisition that does not need to obtain accurate distances between a knee joint and its corresponding sensors is presented. RESULTS: The results showed that there were significant differences between the two groups in the three nominated angle amplitudes. The mean values of balance level of each parameter in hemiplegic gait and normal gait were: 0.21 versus 0.01, 0.18 versus 0.03, and 0.92 versus 0.03, respectively. The mean values of added angles of each parameter in hemiplegic gait and normal gait were: 74.64 versus 91.31, -76.48 versus -132.4, and 6.77 versus 35.74. CONCLUSIONS: It was concluded that the wearable bio-motion acquisition platform provided a practical approach that was effective in discriminating gait symptoms between hemiplegic and asymptomatic subjects. The extensibility of hemiplegic patients' lower limbs was significantly lower than that of normal subjects, and the hemiplegic gait had worse balance level compared with normal gait. The effect of rehabilitation training of hemiplegic gait could be quantitatively analyzed.

Exploration and implementation of a pre-impact fall recognition method based on an inertial body sensor network.

The unintentional injuries due to falls in elderly people give rise to a multitude of health and economic problems due to the growing aging population. The use of early pre-impact fall alarm and self-protective control could greatly reduce fall injuries. This paper aimed to explore and implement a pre-impact fall recognition/alarm method for free-direction fall activities based on understanding of the pre-impact lead time of falls and the angle of body postural stability using an inertial body sensor network. Eight healthy Asian adult subjects were arranged to perform three kinds of daily living activities and three kinds of fall activities. Nine MTx sensor modules were used to measure the body segmental kinematic characteristics of each subject for pre-impact fall recognition/alarm. Our analysis of the kinematic features of human body segments showed that the chest was the optimal sensor placement for an early pre-impact recognition/alarm (i.e., prediction/alarm of a fall event before it happens) and post-fall detection (i.e., detection of a fall event after it already happened). Furthermore, by comparative analysis of threshold levels for acceleration and angular rate, two acceleration thresholds were determined for early pre-impact alarm (7 m/s/s) and post-fall detection (20 m/s/s) under experimental conditions. The critical angles of postural stability of torso segment in three kinds of fall activities (forward, sideway and backward fall) were determined as 23.9 ± 3.3, 49.9 ± 4.1 and 9.9 ± 2.5 degrees, respectively, and the relative average pre-impact lead times were 329 ± 21, 265 ± 35 and 257 ± 36 ms. The results implied that among the three fall activities the sideway fall was associated with the largest postural stability angle and the forward fall was associated with the longest time to adjust body angle to avoid the fall; the backward fall was the most difficult to avoid among the three kinds of fall events due to the toughest combination of shortest lead time and smallest angle of postural stability which made it difficult for the self-protective control mechanism to adjust the body in time to avoid falling down.