Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach?

Wouda, Frank J.; Giuberti, Matteo; Bellusci, Giovanni; Veltink, Petrus H.

doi:10.3390/s16122138

Cited by 52 publications

(80 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Of particular concern are the battery capacity and memory constraints of current wearables. Of the more popular wearable sensors, gyroscopes are notorious for limiting long-term capture due to their power requirements and would thus limit immediate application of several methods reviewed [37,[63][64][65][67][68][69]. Alternatively, accelerometers and sEMG are able to provide continuous recording for at least 24-hours with current battery technology.…”

Section: Concerns For Practical Implementationmentioning

confidence: 99%

Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

Gurchiek¹,

Cheney²,

McGinnis³

2019

Preprint

View full text Add to dashboard Cite

Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption.

show abstract

Section: Concerns For Practical Implementationmentioning

confidence: 99%

Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

Gurchiek¹,

Cheney²,

McGinnis³

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the model has the advantage of a relative simplicity. This allows future investigators to develop a similar algorithm based on wearable sensor data to produce equivalent output: recent papers showed that algorithms merging magnetic, inertial and eventually GPS data from multi-sensor systems can potentially provide the information required to run the algorithm in outdoor conditions (Riaz et al, 2015;Wouda et al, 2016).…”

Section: Practical Implicationsmentioning

confidence: 99%

Kinematic algorithm to determine the energy cost of running with changes of direction

Zago

Esposito

Rausa

et al. 2018

Journal of Biomechanics

View full text Add to dashboard Cite

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. KINEMATIC ALGORITHM TO DETERMINE THE ENERGY COST OF RUNNING WITH CHANGES OF DIRECTION Submission type: original investigation

show abstract

“…There is already evidence that jerk is abnormal in PD (Teulings et al, 1997;Hogan and Sternad, 2009). Other methods of data reduction by feature extraction involve signal processing methods, e.g., wavelet analysis (Joshi et al, 2017), stochastic models, like the Hidden Markov Model (Joshi et al, 2017), or machine-learning algorithms (Wouda et al, 2016), i.e., using Random Forests (Wahid et al, 2015;Kuhner et al, 2016Kuhner et al, , 2017.…”

Section: Introductionmentioning

confidence: 99%

Motion Biomarkers Showing Maximum Contrast Between Healthy Subjects and Parkinson's Disease Patients Treated With Deep Brain Stimulation of the Subthalamic Nucleus. A Pilot Study

et al. 2020

View full text Add to dashboard Cite

Background: Classic motion abnormalities in Parkinson's disease (PD), such as tremor, bradykinesia, or rigidity, are well-covered by standard clinical assessments such as the Unified Parkinson's Disease Rating Scale (UPDRS). However, PD includes motor abnormalities beyond the symptoms and signs as measured by UPDRS, such as the lack of anticipatory adjustments or compromised movement smoothness, which are difficult to assess clinically. Moreover, PD may entail motor abnormalities not yet known. All these abnormalities are quantifiable via motion capture and may serve as biomarkers to diagnose and monitor PD.Objective: In this pilot study, we attempted to identify motion features revealing maximum contrast between healthy subjects and PD patients with deep brain stimulation (DBS) of the nucleus subthalamicus (STN) switched off and on as the first step to develop biomarkers for detecting and monitoring PD patients' motor symptoms. Methods:We performed 3D gait analysis in 7 out of 26 PD patients with DBS switched off and on, and in 25 healthy control subjects. We computed feature values for each stride, related to 22 body segments, four time derivatives, left-right mean vs. difference, and mean vs. variance across stride time. We then ranked the feature values according to their distinguishing power between PD patients and healthy subjects.Results: The foot and lower leg segments proved better in classifying motor anomalies than any other segment. Higher degrees of time derivatives were superior to lower degrees (jerk > acceleration > velocity > displacement). The averaged movements across left and right demonstrated greater distinguishing power than left-right asymmetries. The variability of motion was superior to motion's absolute values.Kuhner et al. Motion Capture in Parkinson's DiseaseConclusions: This small pilot study identified the variability of a smoothness measure, i.e., jerk of the foot, as the optimal signal to separate healthy subjects' from PD patients' gait. This biomarker is invisible to clinicians' naked eye and is therefore not included in current motor assessments such as the UPDRS. We therefore recommend that more extensive investigations be conducted to identify the most powerful biomarkers to characterize motor abnormalities in PD. Future studies may challenge the composition of traditional assessments such as the UPDRS.

show abstract

Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach?

Cited by 52 publications

References 20 publications

Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

Kinematic algorithm to determine the energy cost of running with changes of direction

Motion Biomarkers Showing Maximum Contrast Between Healthy Subjects and Parkinson's Disease Patients Treated With Deep Brain Stimulation of the Subthalamic Nucleus. A Pilot Study

Contact Info

Product

Resources

About