| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/sensors/ZhouFTBEGA20 |
| P356 | DOI | 10.3390/S20154090 |
| P932 | PMC publication ID | 7435687 |
| P698 | PubMed publication ID | 32707987 |
| P50 | author | Urs Granacher | Q41677255 |
| Can Tunca | Q58643624 | ||
| Lin Zhou | Q97687812 | ||
| P2093 | author name string | Bert Arnrich | |
| Eric Fischer | |||
| Cem Ersoy | |||
| Clemens Markus Brahms | |||
| P2860 | cites work | Gait and its assessment in psychiatry | Q28762858 |
| Gait speed and survival in older adults | Q34824320 | ||
| How fast does the Grim Reaper walk? Receiver operating characteristics curve analysis in healthy men aged 70 and over | Q35618840 | ||
| Quantitative gait markers and incident fall risk in older adults | Q37258984 | ||
| Home-based system for physical activity monitoring in patients with multiple sclerosis (Pilot study). | Q37589820 | ||
| Inertial Sensor-Based Robust Gait Analysis in Non-Hospital Settings for Neurological Disorders | Q38371493 | ||
| Validity and repeatability of inertial measurement units for measuring gait parameters | Q38824772 | ||
| Mobile Stride Length Estimation with Deep Convolutional Neural Networks | Q38883431 | ||
| Gait variability and fall risk in community-living older adults: a 1-year prospective study | Q40692815 | ||
| Assessment of energy expenditure for physical activity using a triaxial accelerometer. | Q41010441 | ||
| Inertial sensor-based two feet motion tracking for gait analysis | Q41905282 | ||
| Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring | Q45018597 | ||
| Towards remote monitoring of Parkinson's disease tremor using wearable motion capture systems | Q47242103 | ||
| Zero-Velocity Detection—An Algorithm Evaluation | Q50697483 | ||
| A Mobile Kalman-Filter Based Solution for the Real-Time Estimation of Spatio-Temporal Gait Parameters. | Q50869584 | ||
| Assessment of spatio-temporal parameters during unconstrained walking. | Q51708548 | ||
| Estimating energy expenditure using accelerometers. | Q51930477 | ||
| Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables. | Q52641944 | ||
| Inertial Measurement Units for Clinical Movement Analysis: Reliability and Concurrent Validity. | Q53081065 | ||
| Estimation of spatio-temporal parameters of gait from magneto-inertial measurement units: multicenter validation among Parkinson, mildly cognitively impaired and healthy older adults. | Q55118386 | ||
| Agreement between the spatiotemporal gait parameters of healthy adults from the OptoGait system and a traditional three-dimensional motion capture system | Q57037541 | ||
| Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review | Q60441693 | ||
| Towards Inertial Sensor Based Mobile Gait Analysis: Event-Detection and Spatio-Temporal Parameters | Q61455392 | ||
| Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes | Q62813408 | ||
| Analysis of the performance of 17 algorithms from a systematic review: Influence of sensor position, analysed variable and computational approach in gait timing estimation from IMU measurements. | Q64949484 | ||
| Guidelines for clinical applications of spatio-temporal gait analysis in older adults | Q83350117 | ||
| Validity of the Optogait photoelectric system for the assessment of spatiotemporal gait parameters | Q84594427 | ||
| Comparison of gait characteristics between clinical and daily life settings in children with cerebral palsy | Q89602684 | ||
| Wearable Inertial Measurement Units for Assessing Gait in Real-World Environments | Q90176126 | ||
| Wearable Inertial Sensors to Assess Standing Balance: A Systematic Review | Q90228926 | ||
| Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment | Q91342795 | ||
| Gait analysis with wearables predicts conversion to parkinson disease | Q91793254 | ||
| Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models | Q91929189 | ||
| A Feasibility Study of a Wearable Real-Time Notification System for Self-Awareness of Body-Rocking Behavior | Q92697394 | ||
| Inertial Sensor-Based Lower Limb Joint Kinematics: A Methodological Systematic Review | Q93006979 | ||
| Inertial Measurement Unit-Based Estimation of Foot Trajectory for Clinical Gait Analysis | Q93051235 | ||
| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P433 | issue | 15 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | inertial measurement unit | Q941680 |
| P304 | page(s) | 4090 | |
| P577 | publication date | 2020-07-22 | |
| P1433 | published in | Sensors | Q3478643 |
| P1476 | title | How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications | |
| P478 | volume | 20 |
Search more.