The effect of accelerometer mass in mechanomyography measurements

Mechanomyography (MMG) signals record and quantify low-frequency lateral oscillations of active skeletal muscles. These oscillations reflect the ‘‘mechanical counterpart’’ of the motor unit activity measured by electromyography (EMG). Accelerometers have been commonly used to measure MMG. However, the accelerometer mass can affect the MMG signal. The purpose of this paper was to investigate the relationship of the accelerometer mass and the MMG signal. Thirty-two normal volunteers conducted the maximum voluntary contraction of leg extension. MMG signals were obtained from the rectus femoris muscle using an accelerometer. For each subject, the accelerometer mass was varied from 3, 8, 13, 18, 23 and 28 g. The signals were measured for three seconds with a sampling rate of 1kHz. Results showed that the MMG signal amplitude increased as the accelerometer mass increased. However, the median frequency (MF) of the MMG signal decreased with the increased accelerometer mass. When the accelerometer mass increased from 8 g to 13 g, the amplitude of the MMG signal increased the most, and the MF of the MMG signal decreased the most. However, for accelerometers heavier than 13 g, no significant change was observed in both the amplitude and MF. Based on the present study, the mass of the accelerometer is recommended to not exceed 13 g to properly measure MMG signals

Evaluation of Inertial Sensor-Based Pre-Impact Fall Detection Algorithms Using Public Dataset

In this study, pre-impact fall detection algorithms were developed based on data gathered by a custom-made inertial measurement unit (IMU). Four types of simulated falls were performed by 40 healthy subjects (age: 23.4 ± 4.4 years). The IMU recorded acceleration and angular velocity during all activities. Acceleration, angular velocity, and trunk inclination thresholds were set to 0.9 g, 47.3°/s, and 24.7°, respectively, for a pre-impact fall detection algorithm using vertical angles (VA algorithm); and 0.9 g, 47.3°/s, and 0.19, respectively, for an algorithm using the triangle feature (TF algorithm). The algorithms were validated by the results of a blind test using four types of simulated falls and six types of activities of daily living (ADL). VA and TF algorithms resulted in lead times of 401 ± 46.9 ms and 427 ± 45.9 ms, respectively. Both algorithms were able to detect falls with 100% accuracy. The performance of the algorithms was evaluated using a public dataset. Both algorithms detected every fall in the SisFall dataset with 100% sensitivity). The VA algorithm had a specificity of 78.3%, and TF algorithm had a specificity of 83.9%. The algorithms had higher specificity when interpreting data from elderly subjects. This study showed that algorithms using angles could more accurately detect falls. Public datasets are needed to improve the accuracy of the algorithms

A Novel Short-Time Fourier Transform-Based Fall Detection Algorithm Using 3-Axis Accelerations

The short-time Fourier transform- (STFT-) based algorithm was suggested to distinguish falls from various activities of daily living (ADLs). Forty male subjects volunteered in the experiments including three types of falls and four types of ADLs. An inertia sensor unit attached to the middle of two anterior superior iliac spines was used to measure the 3-axis accelerations at 100 Hz. The measured accelerations were transformed to signal vector magnitude values to be analyzed using STFT. The powers of low frequency components were extracted, and the fall detection was defined as whether the normalized power was less than the threshold (50% of the normal power). Most power was observed at the frequency band lower than 5 Hz in all activities, but the dramatic changes in the power were found only in falls. The specificity of 1–3 Hz frequency components was the best (100%), but the sensitivity was much smaller compared with 4 Hz component. The 4 Hz component showed the best fall detection with 96.9% sensitivity and 97.1% specificity. We believe that the suggested algorithm based on STFT would be useful in the fall detection and the classification from ADLs as well

Expression Profile of Sorghum Genes and <i>Cis</i>-Regulatory Elements under Salt-Stress Conditions

Salinity stress is one of the most important abiotic stresses that causes great losses in crop production worldwide. Identifying the molecular mechanisms of salt resistance in sorghum will help develop salt-tolerant crops with high yields. Sorghum (Sorghum bicolor (L.) Moench) is one of the world’s four major grains and is known as a plant with excellent adaptability to salt stress. Among the various genotypes of sorghum, a Korean cultivar Nampungchal is also highly tolerant to salt. However, little is known about how Nampungchal responds to salt stress. In this study, we measured various physiological parameters, including Na+ and K+ contents, in leaves grown under saline conditions and investigated the expression patterns of differentially expressed genes (DEGs) using QuantSeq analysis. These DEG analyses revealed that genes up-regulated in a 150 mM NaCl treatment have various functions related to abiotic stresses, such as ERF and DREB. In addition, transcription factors such as ABA, WRKY, MYB, and bZip bind to the CREs region of sorghum and are involved in the regulation of various abiotic stress-responsive transcriptions, including salt stress. These findings may deepen our understanding of the mechanisms of salt tolerance in sorghum and other crops