Challenges and advances in the use of wearable sensors for lower extremity biomechanics.

[1]  C. Mazzà,et al.  Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium , 2023, Journal of NeuroEngineering and Rehabilitation.

[2]  K. A. Shorter,et al.  Understanding the Influence of Context on Real-World Walking Energetics , 2023, bioRxiv.

[3]  M. Mayer,et al.  Comparing sparse inertial sensor setups for sagittal-plane walking and running reconstructions , 2023, bioRxiv.

[4]  C. Mazzà,et al.  Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization , 2023, Scientific Data.

[5]  J. Hafer,et al.  Minimizing the Effect of IMU Misplacement With a Functional Orientation Method , 2022, medRxiv.

[6]  Mykel J. Kochenderfer,et al.  Personalizing exoskeleton assistance while walking in the real world , 2022, Nature.

[7]  A. Mcgregor,et al.  Generative deep learning applied to biomechanics: A new augmentation technique for motion capture datasets. , 2022, Journal of biomechanics.

[8]  C. K. Liu,et al.  Rapid bilevel optimization to concurrently solve musculoskeletal scaling, marker registration, and inverse kinematic problems for human motion reconstruction , 2022, bioRxiv.

[9]  M. Hasenjäger,et al.  A Multi-Modal Gait Database of Natural Everyday-Walk in an Urban Environment , 2022, Scientific Data.

[10]  Mark W. Newman,et al.  Investigating walking speed variability of young adults in the real world. , 2022, Gait & posture.

[11]  C. K. Liu,et al.  Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation , 2022, SIGGRAPH Asia.

[12]  Reed D. Gurchiek,et al.  Wearable sensors for remote patient monitoring in orthopedics , 2021, Minerva Orthopedics.

[13]  Darshan S. Shah,et al.  Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint , 2021, Scientific data.

[14]  Reed D. Gurchiek,et al.  Wearables-Only Analysis of Muscle and Joint Mechanics: An EMG-Driven Approach , 2021, bioRxiv.

[15]  Sabato Mellone,et al.  Template-Based Recognition of Human Locomotion in IMU Sensor Data Using Dynamic Time Warping , 2021, Sensors.

[16]  Aaron J. Young,et al.  A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions. , 2021, Journal of biomechanics.

[17]  Vrutangkumar V. Shah,et al.  Measuring freezing of gait during daily-life: an open-source, wearable sensors approach , 2021, Journal of neuroengineering and rehabilitation.

[18]  Güray Gürkan PyTHang: an open-source wearable sensor system for real-time monitoring of head-torso angle for ambulatory applications , 2020, Computer methods in biomechanics and biomedical engineering.

[19]  Vrutangkumar V. Shah,et al.  Laboratory versus daily life gait characteristics in patients with multiple sclerosis, Parkinson’s disease, and matched controls , 2020, Journal of neuroengineering and rehabilitation.

[20]  Dylan Kobsar,et al.  Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review , 2020, Sensors.

[21]  Ryan S McGinnis,et al.  A Gaussian Process Model of Muscle Synergy Functions for Estimating Unmeasured Muscle Excitations Using a Measured Subset , 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  Christine F. Martindale,et al.  CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data , 2020, Frontiers in Bioengineering and Biotechnology.

[23]  Noel C. Perkins,et al.  Determining anatomical frames via inertial motion capture: A survey of methods. , 2020, Journal of biomechanics.

[24]  R. Zernicke,et al.  Propulsive joint powers track with sensor-derived angular velocity: A potential tool for lab-less gait retraining. , 2020, Journal of biomechanics.

[25]  Mahmoud El-Gohary,et al.  Digital Biomarkers of Mobility in Parkinson's Disease During Daily Living. , 2020, Journal of Parkinson's disease.

[26]  N. Colabianchi,et al.  Wearable Sensors Quantify Mobility in People With Lower Limb Amputation During Daily Life , 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[27]  Nikolaus F. Troje,et al.  MoVi: A large multi-purpose human motion and video dataset , 2020, PloS one.

[28]  Jeffrey M. Hausdorff,et al.  Long-term unsupervised mobility assessment in movement disorders , 2020, The Lancet Neurology.

[29]  K. Aminian,et al.  Comparison of gait characteristics between clinical and daily life settings in children with cerebral palsy , 2020, Scientific Reports.

[30]  AD Kuo,et al.  Human walking in the real world: Interactions between terrain type, gait parameters, and energy expenditure , 2019, bioRxiv.

[31]  Ronald F Zernicke,et al.  Measuring markers of aging and knee osteoarthritis gait using inertial measurement units. , 2019, Journal of biomechanics.

[32]  Alberto Leardini,et al.  ISB recommendations on the reporting of intersegmental forces and moments during human motion analysis. , 2019, Journal of biomechanics.

[33]  Reed D. Gurchiek,et al.  Open-Source Remote Gait Analysis: A Post-Surgery Patient Monitoring Application , 2019, Scientific Reports.

[34]  Scott L. Delp,et al.  OpenSim Moco: Musculoskeletal optimal control , 2019, bioRxiv.

[35]  Reed D. Gurchiek,et al.  Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques , 2019, Sensors.

[36]  Antonie J van den Bogert,et al.  Estimation of gait kinematics and kinetics from inertial sensor data using optimal control of musculoskeletal models. , 2019, Journal of biomechanics.

[37]  Peter G. Adamczyk,et al.  Analyzing Gait in the Real World Using Wearable Movement Sensors and Frequently Repeated Movement Paths , 2019, Sensors.

[38]  J. Hafer,et al.  Gait mechanics contribute to exercise induced pain flares in knee osteoarthritis , 2019, BMC Musculoskeletal Disorders.

[39]  H. Shimada,et al.  Relationship between Daily and In-laboratory Gait Speed among Healthy Community-dwelling Older Adults , 2019, Scientific Reports.

[40]  D. V. Van Citters,et al.  Stance and swing phase knee flexion recover at different rates following total knee arthroplasty: An inertial measurement unit study. , 2019, Journal of biomechanics.

[41]  Paris Perdikaris,et al.  Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..

[42]  Bertram Taetz,et al.  Validity, Test-Retest Reliability and Long-Term Stability of Magnetometer Free Inertial Sensor Based 3D Joint Kinematics , 2018, Sensors.

[43]  Jill M van der Meulen,et al.  Free-living and laboratory gait characteristics in patients with multiple sclerosis , 2018, PloS one.

[44]  Bertram Taetz,et al.  IMU-to-Segment Assignment and Orientation Alignment for the Lower Body Using Deep Learning , 2018, Sensors.

[45]  Rainer Wieching,et al.  Comparison between clinical gait and daily‐life gait assessments of fall risk in older people , 2017, Geriatrics & gerontology international.

[46]  Reed D. Gurchiek,et al.  The use of a single inertial sensor to estimate 3-dimensional ground reaction force during accelerative running tasks. , 2017, Journal of biomechanics.

[47]  Mark W. Newman,et al.  An investigation of using mobile and situated crowdsourcing to collect annotated travel activity data in real-word settings , 2017, Int. J. Hum. Comput. Stud..

[48]  B. Galna,et al.  Free-living gait characteristics in ageing and Parkinson’s disease: impact of environment and ambulatory bout length , 2016, Journal of NeuroEngineering and Rehabilitation.

[49]  F. Horak,et al.  Objective Gait and Balance Impairments Relate to Balance Confidence and Perceived Mobility in People With Parkinson Disease , 2016, Physical Therapy.

[50]  Martina Mancini,et al.  Levodopa Is a Double‐Edged Sword for Balance and Gait in People With Parkinson's Disease , 2015, Movement disorders : official journal of the Movement Disorder Society.

[51]  Thomas Seel,et al.  IMU-Based Joint Angle Measurement for Gait Analysis , 2014, Sensors.

[52]  Lynn Rochester,et al.  Independent domains of gait in older adults and associated motor and nonmotor attributes: validation of a factor analysis approach. , 2013, The journals of gerontology. Series A, Biological sciences and medical sciences.

[53]  Cheryl L. Hubley-Kozey,et al.  Effect of a high intensity quadriceps fatigue protocol on knee joint mechanics and muscle activation during gait in young adults , 2012, European Journal of Applied Physiology.

[54]  Angelo M. Sabatini,et al.  Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing , 2006, IEEE Transactions on Biomedical Engineering.

[55]  G M Lyons,et al.  A description of an accelerometer-based mobility monitoring technique. , 2005, Medical engineering & physics.

[56]  Bryan Buchholz,et al.  ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion--Part II: shoulder, elbow, wrist and hand. , 2005, Journal of biomechanics.

[57]  Lorenzo Chiari,et al.  Human movement analysis using stereophotogrammetry. Part 4: assessment of anatomical landmark misplacement and its effects on joint kinematics. , 2005, Gait & posture.

[58]  Catherine Dehollain,et al.  Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring , 2004, IEEE Transactions on Biomedical Engineering.

[59]  Hartmut Witte,et al.  ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion--part I: ankle, hip, and spine. International Society of Biomechanics. , 2002, Journal of biomechanics.

[60]  P R Cavanagh,et al.  ISB recommendations for standardization in the reporting of kinematic data. , 1995, Journal of biomechanics.

[61]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[62]  Fabio Storm,et al.  Moving from laboratory to real life conditions: Influence on the assessment of variability and stability of gait. , 2018, Gait & posture.

[63]  Rachel V. Vitali,et al.  Quantifying performance and effects of load carriage during a challenging balancing task using an array of wireless inertial sensors. , 2016, Gait & posture.

[64]  Bernt Schiele,et al.  A tutorial on human activity recognition using body-worn inertial sensors , 2014, CSUR.

[65]  James McNames,et al.  Mobility Lab to Assess Balance and Gait with Synchronized Body-worn Sensors. , 2011, Journal of bioengineering & biomedical science.

[66]  Herbert K. H. Lee,et al.  Gaussian Processes , 2011, International Encyclopedia of Statistical Science.