OBJECTIVES We aimed to validate a multi–sensor‐based kiosk (automatically measured Short Physical Performance Battery [eSPPB] kiosk) that can perform automated measurement of the SPPB. DESIGN Prospective, cross‐sectional study. SETTING Rehabilitation clinic of a tertiary‐care hospital. PARTICIPANTS Ambulatory outpatients, aged 65 years or older (N = 40). MEASUREMENTS The eSPPB kiosk was developed to measure the three components of the SPPB: standing balance, gait speed, and chair stand test with embedded sensors and algorithms. Correlations between the total and component‐specific scores of the eSPPB and manually measured SPPB (mSPPB), assessed by a physical therapist, were assessed. Further, correlations between SPPB parameters and geriatric functional measures were also evaluated. RESULTS This study included 40 participants with a mean age of 74.4 ± 6.5 years, a mean total eSPPB score of 10.1 ± 2.1, and a mean total mSPPB score of 10.2 ± 2.1. The intraclass correlation coefficient between the eSPPB and mSPPB total score was 0.97 (P < .001), and the κ agreement was 0.79 (P < .001). The intraclass coefficients between the components of eSPPB and mSPPB were 0.77 (P < .001), 0.88 (P < .001), and 0.99 (P < .001) for standing balance, gait speed, and chair stand test, respectively. CONCLUSION The newly developed kiosk might be a viable and efficient method for performing the SPPB in older adults. J Am Geriatr Soc 67:2605–2609, 2019

We report the direct observation of microstructural changes of LixSi electrode with lithium insertion. HRTEM experiments confirm that lithiated amorphous silicon forms a shell around a core made up of the unlithiated silicon and that fully lithiated silicon contains a large number of pores of which concentration increases toward the center of the particle. Chemomechanical modeling is employed in order to explain this mechanical degradation resulting from stresses in the LixSi particles with lithium insertion. Because lithiation‐induced volume expansion and pulverization are the key mechanical effects that plague the performance and lifetime of high‐capacity Si anodes in lithium‐ion batteries, our observations and chemomechanical simulation provide important mechanistic insight for the design of advanced battery materials.

Fig. 1: This paper provides the complex urban data set including metropolitan area, apartment building complex and underground parking lot. Sample scenes from the data set can be found in https://youtu.be/IguZjmLf5V0.Abstract-This paper presents a Light Detection and Ranging (LiDAR) data set that targets complex urban environments. Urban environments with high-rise buildings and congested traffic pose a significant challenge for many robotics applications. The presented data set is unique in the sense it is able to capture the genuine features of an urban environment (e.g. metropolitan areas, large building complexes and underground parking lots). Data of two-dimensional (2D) and threedimensional (3D) LiDAR, which are typical types of LiDAR sensors, are provided in the data set. The two 16-ray 3D LiDARs are tilted on both sides for maximal coverage. One 2D LiDAR faces backward while the other faces forwards to collect data of roads and buildings, respectively. Raw sensor data from Fiber Optic Gyro (FOG), Inertial Measurement Unit (IMU), and the Global Positioning System (GPS) are presented in a file format for vehicle pose estimation. The pose information of the vehicle estimated at 100 Hz is also presented after applying the graph simultaneous localization and mapping (SLAM) algorithm. For the convenience of development, the file player and data viewer in Robot Operating System (ROS) environment were also released via the web page. The full data sets are available at: http://irap.kaist.ac.kr/dataset. In this website, 3D preview of each data set is provided using WebGL.

Background: We aimed to compare 4 automatic devices with a conventional stopwatch for measuring gait speed. Methods: We used 4 experimental devices to automatically measure gait speed: 1) Gaitspeedometer (GSM) 1, with laser sensors; 2) GSM2, with ultrasound sensors; 3) GSM3, with infrared sensors; and 4) GSM4, with a light detection and ranging sensor. To assess compatibility between different versions of GSMs, we collected 426 data points from 4 young engineers walking at random speeds and with varying postures. We used these data to convert gait speed measured by GSM1 and 2 for compatibility with GSM3 in the Korean Frailty and Aging Cohort Study (KFACS) dataset. Results: Mean gait speeds measured with GSMs 1-4 were 1.7% slower (R 2 =0.997), 12.2% faster (R 2 =0.993), 1.3% slower (R 2 =0.999), and 4.3% slower (R 2 =0.996), respectively, than the gait speed measured with a stopwatch. The concordance correlation coefficient between each GSM and the stopwatch was higher than 0.9. Using linear regression analysis with no constant term, conversion formulas for GSMs were established for the KFACS dataset using GSM1 and GSM2. Conclusion: The 4 methods of automatic gait speed measurement and the manually measured gait speed correlated well with each other, and we hope these new technologies reduce barriers to measuring older people's gait speed in busy clinical settings.

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