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.

Study on Tripping Risks in Fast Walking through Cadence-Controlled Gait Analysis.

Fast walking is a common exercise for most people to promote health. However, a higher cadence due to fast walking on ordinary or uneven ground raises the risk of tripping. To investigate the tripping issue, research to observe the gait in fast walking is needed. To explore the relationship between fast gait and the risk of tripping, a gait recording system with a specific synchronization mechanism was developed in this work. The system can acquire gait signals from wearable sensors and action cameras at different cadences. Meanwhile, algorithms for gait cycle segmentation and characteristic extraction were proposed for analyzing a fast gait. In the gait analysis, the correlations of low, moderate, and high cadence in cueing and no cueing gaits were computed, and two results were obtained. First, the higher the cadence is, the larger the motion strength in the terminal foot swing will be and the smaller the motion strength at the starting foot swing. Second, the decreased distance of foot clearance becomes more conspicuous as the cadence increased, especially if one is walking more than 120 beats. The results indicate that fast walking with bigger strides and lower cadence is the best way to maintain safety in moving over ordinary ground.

Comparison of gait symmetry between poststroke fallers and nonfallers during level walking using triaxial accelerometry: A STROBE-compliant cross-sectional study.

To compare the degree of gait symmetry of chronic poststroke fallers with that of nonfallers during level walking using triaxial accelerometry.In this cross-sectional study, a total of 14 patients with chronic stroke were recruited from a community hospital from February 2015 to July 2016. Patient characteristics, including the number of falls in the previous 12 months, were obtained from medical records. The Berg Balance Scale (BBS) and timed up and go (TUG) test were used at the onset of the study. Triaxial accelerometers were attached to the back and bilateral lower extremities of each subject with sampling rates of 120 Hz. The cross-correlation between the acceleration signals of the affected and unaffected feet was measured to assess the degree of gait symmetry. The triaxial acceleration signals of the 5 consecutive and bilateral strides from the middle of each trial were processed to measure the cross-correlation and time delay (Ts) between the magnitude of the acceleration vector of the affected and unaffected foot.After controlling for possible confounding factors, the mixed-effect models showed that cross-correlation was significantly higher among nonfallers than fallers (ß = -0.093; standard error [SE] = 0.029; P-value = 0.002), and that the Ts was significantly longer among fallers than nonfallers (ß = -1.900; SE = 0.719; P-value = 0.011).Cross-correlation and Ts between the affected and unaffected lower extremities may be useful indicators to distinguish poststroke fallers from nonfallers.

Effective Electrocardiogram Steganography Based on Coefficient Alignment.

This study presents two types of data hiding methods based on coefficient alignment for electrocardiogram (ECG) signals, namely, lossy and reversible ECG steganographys. The lossy method is divided into high-quality and high-capacity ECG steganography, both of which are capable of hiding confidential patient data in ECG signals. The reversible data hiding method can not only hide secret messages but also completely restore the original ECG signal after bit extraction. Simulations confirmed that the perceived quality generated by the lossy ECG steganography methods was good, while hiding capacity was acceptable. In addition, these methods have a certain degree of robustness, which is rare in conventional ECG stegangraphy schemes. Moreover, the proposed reversible ECG steganography method can not only successfully extract hidden messages but also completely recover the original ECG data.

Real-time telemetry system for amperometric and potentiometric electrochemical sensors.

A real-time telemetry system, which consists of readout circuits, an analog-to-digital converter (ADC), a microcontroller unit (MCU), a graphical user interface (GUI), and a radio frequency (RF) transceiver, is proposed for amperometric and potentiometric electrochemical sensors. By integrating the proposed system with the electrochemical sensors, analyte detection can be conveniently performed. The data is displayed in real-time on a GUI and optionally uploaded to a database via the Internet, allowing it to be accessed remotely. An MCU was implemented using a field programmable gate array (FPGA) to filter noise, transmit data, and provide control over peripheral devices to reduce power consumption, which in sleep mode is 70 mW lower than in operating mode. The readout circuits, which were implemented in the TSMC 0.18-µm CMOS process, include a potentiostat and an instrumentation amplifier (IA). The measurement results show that the proposed potentiostat has a detectable current range of 1 nA to 100 µA, and linearity with an R2 value of 0.99998 in each measured current range. The proposed IA has a common-mode rejection ratio (CMRR) greater than 90 dB. The proposed system was integrated with a potentiometric pH sensor and an amperometric nitrite sensor for in vitro experiments. The proposed system has high linearity (an R2 value greater than 0.99 was obtained in each experiment), a small size of 5.6 cm × 8.7 cm, high portability, and high integration.