Pattern Classification for Interpreting Sensor Data from a Walking-Speed Robot

2020 ◽  
pp. 247-295
Author(s):  
Eric A. Dieckman ◽  
Mark K. Hinders
Keyword(s):  
Pattern Classification ◽  
Walking Speed ◽  
Sensor Data

A Music-Based Digital Therapeutic: Proof-of-Concept Automation of a Progressive and Individualized Rhythm-Based Walking Training Program After Stroke

2020 ◽  
Vol 34 (11) ◽  
pp. 986-996
Author(s):  
Karen Hutchinson ◽  
Regina Sloutsky ◽  
Ashley Collimore ◽  
Benjamin Adams ◽  
Brian Harris ◽  
...  
Keyword(s):  
Training Program ◽  
Real Time ◽  
Walking Speed ◽  
Brain Regions ◽  
Rehabilitation Program ◽  
Sensor Data ◽  
Locomotor Training ◽  
Proof Of Concept ◽  
Community Based Rehabilitation ◽  
Walking Speeds

Background The rhythm of music can entrain neurons in motor cortex by way of direct connections between auditory and motor brain regions. Objective We sought to automate an individualized and progressive music-based, walking rehabilitation program using real-time sensor data in combination with decision algorithms. Methods A music-based digital therapeutic was developed to maintain high sound quality while modulating, in real-time, the tempo (ie, beats per minute, or bpm) of music based on a user’s ability to entrain to the tempo and progress to faster walking cadences in-sync with the progression of the tempo. Eleven individuals with chronic hemiparesis completed one automated 30-minute training visit. Seven returned for 2 additional visits. Safety, feasibility, and rehabilitative potential (ie, changes in walking speed relative to clinically meaningful change scores) were evaluated. Results A single, fully automated training visit resulted in increased usual (∆ 0.085 ± 0.027 m/s, P = .011) and fast (∆ 0.093 ± 0.032 m/s, P = .016) walking speeds. The 7 participants who completed additional training visits increased their usual walking speed by 0.12 ± 0.03 m/s after only 3 days of training. Changes in walking speed were highly related to changes in walking cadence ( R2 > 0.70). No trips or falls were noted during training, all users reported that the device helped them walk faster, and 70% indicated that they would use it most or all of the time at home. Conclusions In this proof-of-concept study, we show that a sensor-automated, progressive, and individualized rhythmic locomotor training program can be implemented safely and effectively to train walking speed after stroke. Music-based digital therapeutics have the potential to facilitate salient, community-based rehabilitation.

Auditory visualization of landmine detector sensor data

2009 ◽  
Author(s):  
Bradley M. Davis ◽  
Woodrow W. Winchester ◽  
Jason D. Zedlitz
Keyword(s):  
Sensor Data

Kinetic alterations independent of walking speed in elderly fallers

2000 ◽  
Vol 81 (6) ◽  
pp. 730-735
Author(s):  
D. Casey Kerrigan ◽  
Laura W. Lee ◽  
Tanya J. Nieto ◽  
John D. Markman ◽  
James J. Collins ◽  
...  
Keyword(s):  
Walking Speed

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

Sign in / Sign up

Export Citation Format

Share Document