Accuracy of Estimating Hand Location During Lifting Using Five Wearable Motion Sensors

2020 ◽  
Vol 64 (1) ◽  
pp. 1269-1273
Author(s):  
Menekse S. Barim ◽  
Ming-Lun Lu ◽  
Shuo Feng ◽  
Grant Hughes ◽  
Marie Hayden ◽  
...  
Keyword(s):  
The Body ◽  
Motion Sensors ◽  
Body Segment ◽  
Limit Values ◽  
Body Segments ◽  
Motion Data ◽  
Average Accuracy ◽  
Measurement Units ◽  
Using Data ◽  
Wearable Motion Sensors

The purpose of this study was to assess two computation models for estimating the hand locations during lifting tasks using data from five inertial measurement units (IMUs) attached to five body segments. The first model computed the hand location with the IMU gyroscope data and the pre-defined ratios of body segment lengths. The second model used the same gyroscope information and all measured lengths of the body segments. The outcome measure of these models was the estimated hand location in 12 lifting zones defined by the ACGIH Threshold Limit Values (TLVs) for lifting. Motion data was collected with the wearable system and a laboratory-grade motion capture system on ten subjects that performed 12 two- handed lifting tasks representing the lifting zones. By including body segment measurements, the average accuracy of the model improved from 4 to 34%, suggesting that body segment information plays an important role in estimating the lifting zones.

scholarly journals Validation method for body-mounted sensor attachments

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Katharina Schmidt ◽  
David Hochmann
Keyword(s):  
Evaluation Method ◽  
Tracking System ◽  
The Body ◽  
Exploratory Research ◽  
Body Segment ◽  
Validation Method ◽  
Sensor Position ◽  
Validation Parameters ◽  
Measurement Units ◽  
Soft Tissue Artifact

AbstractSmall sensor devices like inertial measurement units enable mobile movement and gait analysis, whereby existing systems differ in data acquisition, data processing, and gait parameter calculation. Concerning the validation, recent studies focus on the captured motion and the influence of sensor positioning with respect to the accuracy of the computed biomechanical parameters in comparison to a reference system. Although soft tissue artifact is a major source of error for skin-mounted sensors, there are no investigations regarding the relative movement between the body segment and sensor attachment itself. The aim of this study is to find an evaluation method and to determine parameters that allow the validation of various sensor attachment types and different sensor positionings. The analysis includes the comparison between an adhesive and strap attachment variant as well as the frontal and lateral sensor placement. To validate different attachments, an optical marker-based tracking system was used to measure the body segment and sensor position during movement. The distance between these two positions was calculated and analyzed to determine suitable validation parameters. Despite the exploratory research, the results suggest a feasible validation method to detect differences between the attachments, independent of the sensor type. To have representative and statistically validated results, further studies that involve more participants are necessary.

scholarly journals Development of an Algorithm for Automatically Assessing Lifting Risk Factors Using Inertial Measurement Units

2019 ◽  
Vol 63 (1) ◽  
pp. 1334-1338 ◽  
Author(s):  
Ming-Lun Lu ◽  
Shuo Feng ◽  
Grant Hughes ◽  
Menekse S. Barim ◽  
Marie Hayden ◽  
...  
Keyword(s):  
Risk Factors ◽  
The Body ◽  
Measurement Unit ◽  
Trunk Flexion ◽  
Inertial Measurement ◽  
Motion Data ◽  
Low Back Disorders ◽  
Machine Learning Approach ◽  
Measurement Units ◽  
Forearm Length

The objective of this study was to develop an algorithm for automatically processing data collected with inertial measurement unit (IMU) wearable devices to measure lifting risk factors for low back disorders. Five IMU sensors attached to five body segments were used for developing the algorithm. The algorithm consists of two modules running in parallel for detecting the beginning and ending of a lifting event as well as the vertical height (V) of the object lifted by two hands and the horizontal (H) distance between the object and the body during the lift. The motion synchronization feature of wrists’ motion data were used to train the lifting detection module using a machine learning approach. This module achieved a training accuracy of 85%. In the second module, the forearm length and gyroscope data of four sensors are proposed for calculating trunk flexion angle, V and H during a lift.

scholarly journals Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras

2014 ◽  
Author(s):  
Kathrin E Peyer ◽  
Mark Morris ◽  
William I Sellers
Keyword(s):  
Regression Models ◽  
Point Cloud ◽  
The Body ◽  
Reconstruction Algorithms ◽  
Multiple Cameras ◽  
Body Segment ◽  
Body Segments ◽  
Subject Specific ◽  
3D Photogrammetry ◽  
Body Segment Parameters

Inertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. These quantities are, however, not directly measurable. Current approaches include using regression models which have limited accuracy; geometric models with lengthy measuring procedures; or acquiring and post-processing MRI scans of participants. We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. The point cloud was manually separated into body segments and convex hulling applied to each segment to produce the required geometric outlines. The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments. The body segment parameters of six participants (four male and two female) are presented using the proposed method. The multi-camera photogrammetric approach is expected to be particularly suited for studies including populations for which regression models are not available in literature and where other geometric techniques or MRI scanning are not applicable due to time or ethical constraints.

Monopodal Postural Stability Assessment by Wireless Inertial Measurement Units Through the Fast Fourier Transform

2020 ◽  
Vol 29 (6) ◽  
pp. 738-747
Author(s):  
José Pino-Ortega ◽  
Alejandro Hernández-Belmonte ◽  
Carlos D. Gómez-Carmona ◽  
Alejandro Bastida-Castillo ◽  
Javier García-Rubio ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Lumbar Spine ◽  
Fast Fourier Transform ◽  
Lower Limb ◽  
Postural Stability ◽  
National Level ◽  
The Body ◽  
Stability Assessment ◽  
Body Segment ◽  
Body Segments

Objectives: (1) To describe the fast Fourier transform (FFT) multijoint as monopodal postural stability measurement in well-trained athletes, (2) to compare the within-subject FFT between laterality, joints, and body segments, and (3) to establish the within- and between-subject relationship between joints. Methods: Twelve national-level basketball players participated voluntarily in this investigation. The participants performed two 60-second repetitions of a monopodal stability test (1 repetition with each lower limb), separated by 3 minutes of active recovery. All tests were recorded by 4 WIMU PRO™ inertial devices located on the ankle, knee, lumbar spine, and thoracic spine. The main variable was total acceleration, where the FFT was applied. Results: The higher instability results were found in the ankle and in the nondominant lower limb (dominant = 1.131 [0.122] a.u. (arbitrary units); nondominant = 1.141 [0.172] a.u). In the body segment analysis, the greater percentage of differences (%diff) were shown between lumbar spine and knee in the dominant (%diff = −2.989%; d = 0.87) and nondominant (%diff = −3.243%; d = 0.90) lower limb. Finally, very large between-subjects variability was found in all joints and body segments. Conclusions: The described protocol is proposed for monopodal postural stability assessment, being useful to provide information about the stability of joints and the body segment between joints. Besides, a within-subject analysis is recommended, and the FFT calculation will enable a linear analysis of each test.

scholarly journals Current clinical utilisation of wearable motion sensors for the assessment of outcome following knee arthroplasty: a scoping review

BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e033832 ◽  
Author(s):  
Scott R Small ◽  
Garrett S Bullock ◽  
Sara Khalid ◽  
Karen Barker ◽  
Marialena Trivella ◽  
...  
Keyword(s):  
Physical Activity ◽  
Knee Arthroplasty ◽  
Scoping Review ◽  
Motion Sensors ◽  
Patient Assessment ◽  
Inertial Measurement ◽  
Testing Protocol ◽  
Scoping Reviews ◽  
Measurement Units ◽  
Wearable Motion Sensors

ObjectivesWearable motion sensors are used with increasing frequency in the evaluation of gait, function and physical activity within orthopaedics and sports medicine. The integration of wearable technology into the clinical pathway offers the ability to improve post-operative patient assessment beyond the scope of current, questionnaire-based patient-reported outcome measures. This scoping review assesses the current methodology and clinical application of accelerometers and inertial measurement units for the evaluation of patient activity and functional recovery following knee arthroplasty.DesignThis is a systematically conducted scoping review following Joanna Briggs Institute methodology for scoping reviews and reported consulting the Preferred Reporting Items for Systematic Review and Meta-Analyses extension for scoping reviews. A protocol for this review is registered with the Open Science Framework (https://osf.io/rzg9q).Data sourcesCINAHL, EMBASE, MEDLINE and Web of Science databases were searched for manuscripts published between 2008 and 2019.Eligibility criteriaWe included clinical studies reporting the use of any combination of accelerometers, pedometers or inertial measurement units for patient assessment at any time point following knee arthroplasty.Data extraction and synthesisData extracted from manuscripts included patient demographics, sensor technology, testing protocol and sensor-based outcome variables.Results45 studies were identified, including 2076 knee arthroplasty patients, 620 patients with end-stage osteoarthritis and 449 healthy controls. Primary aims of the identified studies included functional assessment, physical activity monitoring and evaluation of knee instability. Methodology varied widely between studies, with inconsistency in reported sensor configuration, testing protocol and output variables.ConclusionsThe use of wearable sensors in evaluation of knee arthroplasty procedures is becoming increasingly common and offers the potential to improve clinical understanding of recovery and rehabilitation. While current studies lack consistency, significant opportunity exists for the development of standardised measures and protocols for function and physical activity evaluation.

scholarly journals Elaboration and assessment of clinical protocols to support the evaluation of stand-to-sit activity

2014 ◽  
Vol 27 (2) ◽  
pp. 251-259 ◽  
Author(s):  
Maíra Junkes Cunha ◽  
Carolina Mendes do Carmo ◽  
Cássio Marinho Siqueira ◽  
Kelly Takara ◽  
Clarice Tanaka
Keyword(s):  
Healthy Subjects ◽  
Physical Therapists ◽  
The Body ◽  
Neutral Position ◽  
Upper Limbs ◽  
Clinical Protocols ◽  
Body Segment ◽  
Body Segments ◽  
Sit To Stand ◽  
And Inversion

Introduction Evaluation of sit-to-stand and stand-to-sit activities is used by physical therapists in patients with neurological and musculoskeletal disorders. Sit-to-stand activity presents different descriptions of phases and movements; however the phases of stand-to-sit activity have not been established yet.Objectives To describe the movements during stand-to-sit activity and create an evaluation protocol.Materials and methods Stand-to-sit activity was described on anterior and lateral views based on the observation of 27 healthy subjects. The body segments chosen to analyze were feet, ankles, knees, hips, pelvis, trunk, spine, upper limbs, head and cervical spine. The movements of body segments were described as adduction and abduction, eversion and inversion, valgus and varus, neutral position and asymmetry. The protocol was assessed with questionnaires answered by 12 physiotherapists experts in the area.Results Stand-to-sit activity was divided in 4 phases: 1- "Neutral position", 2- "Pre-squat", 3- "Squat" and 4- "Stabilization". Two models of protocols were developed considering 5 body segments to the anterior view and 7 segments for the lateral view.Conclusion Stand-to-sit activity was described in 4 phases with sequential movements of each body segment. These protocols allow physiotherapists to identify unusual movements of body segments during the stand-to-sit activity.

scholarly journals The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5625
Author(s):  
Sylvain Jung ◽  
Mona Michaud ◽  
Laurent Oudre ◽  
Eric Dorveaux ◽  
Louis Gorintin ◽  
...  
Keyword(s):  
Physical Activity ◽  
Living Conditions ◽  
Living Environment ◽  
Motion Sensors ◽  
Free Living ◽  
Inertial Measurement ◽  
Activity Assessment ◽  
Measurement Units ◽  
Wearable Motion Sensors ◽  
Free Living Conditions

This article presents an overview of fifty-eight articles dedicated to the evaluation of physical activity in free-living conditions using wearable motion sensors. This review provides a comprehensive summary of the technical aspects linked to sensors (types, number, body positions, and technical characteristics) as well as a deep discussion on the protocols implemented in free-living conditions (environment, duration, instructions, activities, and annotation). Finally, it presents a description and a comparison of the main algorithms and processing tools used for assessing physical activity from raw signals.

scholarly journals Multi-location external workload profile in U-18 soccer players. [Perfil multi-ubicación de carga externa en jugadores de fútbol sub-18].

2021 ◽  
Vol 17 (64) ◽  
pp. 124-139
Author(s):  
Carlos David Gómez-Carmona ◽  
Alejandro Bastida-Castillo ◽  
Víctor Moreno-Pérez ◽  
Sergio José Ibáñez ◽  
José Pino-Ortega
Keyword(s):  
Lower Limb ◽  
Injury Risk ◽  
Cross Sectional Study ◽  
The Body ◽  
Soccer Players ◽  
Body Segment ◽  
Cross Sectional ◽  
Lower Upper ◽  
Youth Soccer ◽  
Body Segments

An association between accelerometer workload and injury risk has been found previously. However, any research has assessed the absorption dynamics of external workload through the measurement in different anatomical locations simultaneously. A cross-sectional study was designed to: (i) to describe the multi-joint external workload profile of youth soccer players, (ii) to identify differences between-participants related to anatomical locations, (iii) to analyze the workload dynamics at different speeds at joints and body segments, (iv) to characterize the multi-joint individual workload and the within-participants difference in each body segment. Twenty-one U-18 male players, that were part of a Youth Spanish First Division soccer team, performed an incremental running treadmill test and wore four WIMU PROTM inertial devices in lower limb (ankle-knee) and spine (lower-upper back) locations to register cumulative tri-axial accelerometry-based workload (PlayerLoad, PLRT). The main results have shown that the highest PLRT was detected at the lower limb, especially at the ankle. Different dynamics of accelerometer workload have been found between lower and upper limb, being them between ankle-knee at 12-km/h and lower-upper back at 9.5-km/h (p<.05). Between-participants’ differences were shown at all joints, finding the highest differences at the upper back (p<.01; d=2.17). Finally, the body segment knee-lower back reported the highest differences (%diff=34.25-to-67.28; d=2.20-to-4.77). In conclusion, a great between-participants external workload variability was found at joints and body segments, being recommended for an individualized assessment and specific training protocols.

scholarly journals Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras

2014 ◽  
Author(s):  
Kathrin E Peyer ◽  
Mark Morris ◽  
William I Sellers
Keyword(s):  
Regression Models ◽  
Point Cloud ◽  
The Body ◽  
Reconstruction Algorithms ◽  
Multiple Cameras ◽  
Body Segment ◽  
Body Segments ◽  
Subject Specific ◽  
3D Photogrammetry ◽  
Body Segment Parameters

Inertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. These quantities are, however, not directly measurable. Current approaches include using regression models which have limited accuracy; geometric models with lengthy measuring procedures; or acquiring and post-processing MRI scans of participants. We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. The point cloud was manually separated into body segments and convex hulling applied to each segment to produce the required geometric outlines. The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments. The body segment parameters of six participants (four male and two female) are presented using the proposed method. The multi-camera photogrammetric approach is expected to be particularly suited for studies including populations for which regression models are not available in literature and where other geometric techniques or MRI scanning are not applicable due to time or ethical constraints.

scholarly journals Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 798
Author(s):  
Hamed Darbandi ◽  
Filipe Serra Bragança ◽  
Berend Jan van der Zwaag ◽  
John Voskamp ◽  
Annik Imogen Gmel ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Biomechanical Analysis ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Motion Patterns ◽  
Positioning Systems ◽  
Machine Learning Approach ◽  
Global Positioning ◽  
Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

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