Tremor analysis by decomposition of acceleration into gravity and inertial acceleration using inertial measurement unit

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

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Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Sensors ◽

10.3390/s21144767 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4767

Author(s):

Karla Miriam Reyes Leiva ◽

Milagros Jaén-Vargas ◽

Benito Codina ◽

José Javier Serrano Olmedo

Keyword(s):

Inertial Measurement Unit ◽

Visually Impaired ◽

Inertial Sensors ◽

Assistive Technologies ◽

Biomechanical Analysis ◽

Measurement Unit ◽

Inertial Measurement ◽

Visually Impaired People ◽

Impaired People ◽

Application Fields

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

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Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data

Sensors ◽

10.3390/s21062246 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2246

Author(s):

Scott Pardoel ◽

Gaurav Shalin ◽

Julie Nantel ◽

Edward D. Lemaire ◽

Jonathan Kofman

Keyword(s):

Early Detection ◽

Plantar Pressure ◽

Inertial Measurement Unit ◽

Binary Classification ◽

Freezing Of Gait ◽

Pressure Sensors ◽

Measurement Unit ◽

Distribution Data ◽

Inertial Measurement ◽

Plantar Pressure Distribution

Freezing of gait (FOG) is a sudden and highly disruptive gait dysfunction that appears in mid to late-stage Parkinson’s disease (PD) and can lead to falling and injury. A system that predicts freezing before it occurs or detects freezing immediately after onset would generate an opportunity for FOG prevention or mitigation and thus enhance safe mobility and quality of life. This research used accelerometer, gyroscope, and plantar pressure sensors to extract 861 features from walking data collected from 11 people with FOG. Minimum-redundancy maximum-relevance and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or No FOG states, wherein the Total-FOG class included data windows from 2 s before the FOG onset until the end of the FOG episode. Three feature sets were compared: plantar pressure, inertial measurement unit (IMU), and both plantar pressure and IMU features. The plantar-pressure-only model had the greatest sensitivity and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, Freeze windows, transition windows between Pre-FOG and Freeze). The best model detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Windows of FOG data were detected with 93.4% sensitivity. The IMU and plantar pressure feature-based model slightly outperformed models that used data from a single sensor type. The model achieved early detection by identifying the transition from Pre-FOG to FOG while maintaining excellent FOG detection performance (93.4% sensitivity). Therefore, if used as part of an intelligent, real-time FOG identification and cueing system, even if the Pre-FOG state were missed, the model would perform well as a freeze detection and cueing system that could improve the mobility and independence of people with PD during their daily activities.

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Design and validation of an inertial measurement unit (IMU)-based sensor for capturing camera movement in the operating room

HardwareX ◽

10.1016/j.ohx.2021.e00179 ◽

2021 ◽

Vol 9 ◽

pp. e00179

Author(s):

Tomas J. Saun ◽

Teodor P. Grantcharov

Keyword(s):

Operating Room ◽

Inertial Measurement Unit ◽

Measurement Unit ◽

Inertial Measurement

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Trunk Posture during Manual Materials Handling of Beer Kegs

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18147380 ◽

2021 ◽

Vol 18 (14) ◽

pp. 7380

Author(s):

Colleen Brents ◽

Molly Hischke ◽

Raoul Reiser ◽

John Rosecrance

Keyword(s):

Small Businesses ◽

Inertial Measurement Unit ◽

Measurement Unit ◽

Low Back ◽

Inertial Measurement ◽

Materials Handling ◽

Kinematic Measurement ◽

Manual Materials Handling ◽

Trunk Posture ◽

Data Design

Craft brewing is a rapidly growing industry in the U.S. Most craft breweries are small businesses with few resources for robotic or other mechanical-assisted equipment, requiring work to be performed manually by employees. Craft brewery workers frequently handle stainless steel half-barrel kegs, which weigh between 13.5 kg (29.7 lbs.) empty and 72.8 kg (161.5 lbs.) full. Moving kegs may be associated with low back pain and even injury. In the present study, researchers performed a quantitative assessment of trunk postures using an inertial measurement unit (IMU)-based kinematic measurement system while workers lifted kegs at a craft brewery. Results of this field-based study indicated that during keg handling, craft brewery workers exhibited awkward and non-neutral trunk postures. Based on the results of the posture data, design recommendations were identified to reduce the hazardous exposure for musculoskeletal disorders among craft brewery workers.

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