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Estimation of Gait Parameters for Transfemoral Amputees Using Lower Limb Kinematics and Deterministic Algorithms. Appl Bionics Biomech 2022; 2022:2883026. [PMID: 36312314 PMCID: PMC9605832 DOI: 10.1155/2022/2883026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 08/05/2022] [Accepted: 09/01/2022] [Indexed: 11/17/2022] Open
Abstract
Accurate estimation of gait parameters depends on the prediction of key gait events of heel strike (HS) and toe-off (TO). Kinematics-based gait event estimation has shown potential in this regard, particularly using leg and foot velocity signals and gyroscopic sensors. However, existing algorithms demonstrate a varying degree of accuracy for different populations. Moreover, the literature lacks evidence for their validity for the amputee population. The purpose of this study is to evaluate this paradigm to predict TO and HS instants and to propose a new algorithm for gait parameter estimation for the amputee population. An open data set containing marker data of 12 subjects with unilateral transfemoral amputation during treadmill walking was used, containing around 3400 gait cycles. Five deterministic algorithms detecting the landmarks (maxima, minima, and zero-crossings [ZC]) in the foot, shank, and thigh angular velocity data indicating HS and TO events were implemented and their results compared against the reference data. Two algorithms based on foot and shank velocity minima performed exceptionally well for the HS prediction, with median accuracy in the range of 6–13 ms. However, both these algorithms produced inferior accuracy for the TO event with consistent early prediction. The peak in the thigh velocity produced the best result for the TO prediction with <25 ms median error. By combining the HS prediction using shank velocity and TO prediction from the thigh velocity, the algorithm produced the best results for temporal gait parameters (step, stride times, stance, and double support timings) with a median error of less than 25 ms. In conclusion, combined shank and thigh velocity-based prediction leads to improved gait parameter estimation than traditional algorithms for the amputee population.
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Bonci T, Salis F, Scott K, Alcock L, Becker C, Bertuletti S, Buckley E, Caruso M, Cereatti A, Del Din S, Gazit E, Hansen C, Hausdorff JM, Maetzler W, Palmerini L, Rochester L, Schwickert L, Sharrack B, Vogiatzis I, Mazzà C. An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks. Front Bioeng Biotechnol 2022; 10:868928. [PMID: 35721859 PMCID: PMC9201978 DOI: 10.3389/fbioe.2022.868928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson’s disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole signals. Sensitivity, positive predictive values (PPV), F1-score, bias, precision, and accuracy were calculated. Absolute agreement [intraclass correlation coefficient (ICC2,1)] between marker-based and pressure insole stride parameters was also tested. In the YA cohort, the proposed method outperformed the existing ones, with sensitivity, PPV, and F1 scores ≥ 99% for both GEs and conditions, with a virtually null bias (<10 ms). Overall, temporal inaccuracies minimally impacted stride duration, length, and speed (median absolute errors ≤1%). Similar algorithm performances were obtained for all the other five cohorts in GE detection and propagation to the stride parameters, where an excellent absolute agreement with the pressure insoles was also found (ICC2,1=0.817− 0.999). In conclusion, the proposed method accurately detects GE from marker data under different walking conditions and for a variety of gait impairments.
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Affiliation(s)
- Tecla Bonci
- Department of Mechanical Engineering, Insigno Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- *Correspondence: Tecla Bonci,
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Kirsty Scott
- Department of Mechanical Engineering, Insigno Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Clemens Becker
- Department for Geriatric Rehabilitation, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Ellen Buckley
- Department of Mechanical Engineering, Insigno Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Marco Caruso
- Department of Electronics and Telecommunications, Politecnico Di Torino, Torino, Italy
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico Di Torino, Torino, Italy
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Eran Gazit
- Centre for the Study of Movement, Cognition and Mobility, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel
| | - Clint Hansen
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel University, Kiel, Germany
| | - Jeffrey M. Hausdorff
- Centre for the Study of Movement, Cognition and Mobility, Tel Aviv Sourasky Medical Centre, Tel Aviv, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine, Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Orthopaedic Surgery, Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, United States
| | - Walter Maetzler
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel University, Kiel, Germany
| | - Luca Palmerini
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies–Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - Lars Schwickert
- Department for Geriatric Rehabilitation, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Basil Sharrack
- Department of Neuroscience, Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle Upon Tyne, United Kingdom
| | - Claudia Mazzà
- Department of Mechanical Engineering, Insigno Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
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Aftab Z, Shad R. Estimation of gait parameters using leg velocity for amputee population. PLoS One 2022; 17:e0266726. [PMID: 35560138 PMCID: PMC9106160 DOI: 10.1371/journal.pone.0266726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 03/28/2022] [Indexed: 11/18/2022] Open
Abstract
Quantification of key gait parameters plays an important role in assessing gait deficits in clinical research. Gait parameter estimation using lower-limb kinematics (mainly leg velocity data) has shown promise but lacks validation for the amputee population. The aim of this study is to assess the accuracy of lower-leg angular velocity to predict key gait events (toe-off and heel strike) and associated temporal parameters for the amputee population. An open data set of reflexive markers during treadmill walking from 10 subjects with unilateral transfemoral amputation was used. A rule-based dual-minima algorithm was developed to detect the landmarks in the shank velocity signal indicating toe-off and heel strike events. Four temporal gait parameters were also estimated (step time, stride time, stance and swing duration). These predictions were compared against the force platform data for 3000 walking cycles from 239 walking trials. Considerable accuracy was achieved for the HS event as well as for step and stride timings, with mean errors ranging from 0 to -13ms. The TO prediction exhibited a larger error with its mean ranging from 35-81ms. The algorithm consistently predicted the TO earlier than the actual event, resulting in prediction errors in stance and swing timings. Significant differences were found between the prediction for sound and prosthetic legs, with better TO accuracy on the prosthetic side. The prediction accuracy also appeared to improve with the subjects’ mobility level (K-level). In conclusion, the leg velocity profile, coupled with the dual-minima algorithm, can predict temporal parameters for the transfemoral amputee population with varying degrees of accuracy.
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Affiliation(s)
- Zohaib Aftab
- Department of Mechanical Engineering, Faculty of Engineering, University of Central Punjab, Lahore, Pakistan
- Human-centered robotics lab, National Center of Robotics and Automation (NCRA), Rawalpindi, Pakistan
- * E-mail:
| | - Rizwan Shad
- Department of Mechanical Engineering, Faculty of Engineering, University of Central Punjab, Lahore, Pakistan
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Is the Prevalence of Equinus Foot in Cerebral Palsy Overestimated? Results from a Meta-Analysis of 4814 Feet. J Clin Med 2021; 10:jcm10184128. [PMID: 34575239 PMCID: PMC8465417 DOI: 10.3390/jcm10184128] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/06/2021] [Accepted: 09/06/2021] [Indexed: 01/04/2023] Open
Abstract
Background: Equinus is a common foot deformity in patients with cerebral palsy (CP). However, its prevalence is scarcely reported in the literature. Therefore, we conducted this review to estimate the prevalence of equinus foot in CP. Methods: Eight databases were searched. Our primary outcome was the prevalence of equinus foot in CP patients. Subgroup analysis was conducted based on study design, the laterality of CP, and whether equinus foot was defined or not. Results: The prevalence of equinus foot in CP was 93% (95% CI: 71–99). The prevalence was 99% (95% CI: 55–100), 96% (95% CI: 57–100), and 65% (95% CI: 37–86) in unilateral, both, and bilateral CP, respectively. Based on study design, equinus foot prevalence was 92% (95% CI: 34–100) in case series and 62% (95% CI: 47–74) in cohort studies. Four studies reported definition criteria for equinus foot, with a pooled prevalence rate of equinus foot of 99% (95% CI: 36–100) compared to a rate of 89% (95% CI: 59–98) among studies that lacked a definition criterion. Conclusions: This is the first meta-analysis to address the prevalence of equinus foot in CP patients. Although its prevalence is very high, our findings should be interpreted with caution due to the presence of multiple limitations, such as the lack of standardized definition criteria for equinus foot, the inappropriate study design, the wide confidence interval of equinus foot rate, and the small number of studies investigating it as a primary outcome.
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Guinet AL, Bouyer G, Otmane S, Desailly E. Validity of Hololens Augmented Reality Head Mounted Display for Measuring Gait Parameters in Healthy Adults and Children with Cerebral Palsy. SENSORS 2021; 21:s21082697. [PMID: 33920452 PMCID: PMC8069043 DOI: 10.3390/s21082697] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 03/31/2021] [Accepted: 04/08/2021] [Indexed: 12/02/2022]
Abstract
Serious games are a promising approach to improve gait rehabilitation for people with gait disorders. Combined with wearable augmented reality headset, serious games for gait rehabilitation in a clinical setting can be envisaged, allowing to evolve in a real environment and provide fun and feedback to enhance patient’s motivation. This requires a method to obtain accurate information on the spatiotemporal gait parameters of the playing patient. To this end, we propose a new algorithm called HoloStep that computes spatiotemporal gait parameters using only the head pose provided by an augmented reality headset (Hololens). It is based on the detection of peaks associated to initial contact event, and uses a combination of locking distance, locking time, peak amplitude detection with custom thresholds for children with CP. The performance of HoloStep was compared during a walking session at comfortable speed to Zeni’s reference algorithm, which is based on kinematics and a full 3D motion capture system. Our study included 62 children with cerebral palsy (CP), classified according to Gross Motor Function Classification System (GMFCS) between levels I and III, and 13 healthy participants (HP). Metrics such as sensitivity, specificity, accuracy and precision for step detection with HoloStep were above 96%. The Intra-Class Coefficient between steps length calculated with HoloStep and the reference was 0.92 (GMFCS I), 0.86 (GMFCS II/III) and 0.78 (HP). HoloStep demonstrated good performance when applied to a wide range of gait patterns, including children with CP using walking aids. Findings provide important insights for future gait intervention using augmented reality games for children with CP.
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Affiliation(s)
- Anne-Laure Guinet
- Pôle Recherche & Innovation, Fondation Ellen Poidatz, 77310 Saint-Fargeau-Ponthierry, France;
- IBISC Lab, University Paris-Saclay, University Evry, 91020 Evry, France; (G.B.); (S.O.)
- Correspondence:
| | - Guillaume Bouyer
- IBISC Lab, University Paris-Saclay, University Evry, 91020 Evry, France; (G.B.); (S.O.)
| | - Samir Otmane
- IBISC Lab, University Paris-Saclay, University Evry, 91020 Evry, France; (G.B.); (S.O.)
| | - Eric Desailly
- Pôle Recherche & Innovation, Fondation Ellen Poidatz, 77310 Saint-Fargeau-Ponthierry, France;
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Lempereur M, Rousseau F, Rémy-Néris O, Pons C, Houx L, Quellec G, Brochard S. A new deep learning-based method for the detection of gait events in children with gait disorders: Proof-of-concept and concurrent validity. J Biomech 2019; 98:109490. [PMID: 31740015 DOI: 10.1016/j.jbiomech.2019.109490] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 10/16/2019] [Accepted: 11/04/2019] [Indexed: 10/25/2022]
Abstract
The stance and swing phases of the gait cycle are defined by foot strike (FS) and foot off (FO). Accurate determination of these events is thus an essential component of 3D motion recordings processing. Several methods have been developed for the automatic detection of these events (based on the heuristics of 3D marker position, velocity and acceleration), however the results may be inaccurate due to the high variability that is intrinsic to pathological gait. For this reason, gait events are still commonly determined manually, which is a tedious process. Here we propose a new application (DeepEvent) of a long short term memory recurrent neural network for the automatic detection of gait events. The 3D position and velocity of the markers on the heel, toe and lateral malleolus were used by the network to determine FS and FO. The method was developed from 10526 FS and 9375 FO from 226 children. DeepEvent predicted FS within 5.5 ms and FO within 10.7 ms of the gold standard (automatic determination using force platform data) and was more accurate than common heuristic marker trajectory-based methods proposed in the literature and another deep learning method. A sensitivity analysis showed that DeepEvent mainly used the toe and heel markers (z-axis (longitudinal) position and velocity) at the beginning and end of gait cycle to predict FS, and the toe marker (x-axis (anterior/posterior) velocity and z-axis position and velocity) at around 60% of the gait cycle to predict FO.
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Affiliation(s)
- Mathieu Lempereur
- Laboratoire de Traitement de l'Information Médicale, INSERM U1101, Brest, France; Université de Bretagne Occidentale, Brest, France; CHRU de Brest, Hôpital Morvan, service de médecine physique et de réadaptation, Brest, France.
| | - François Rousseau
- Laboratoire de Traitement de l'Information Médicale, INSERM U1101, Brest, France; IMT Atlantique, LaTIM U1101 INSERM, UBL, Brest, France
| | - Olivier Rémy-Néris
- Laboratoire de Traitement de l'Information Médicale, INSERM U1101, Brest, France; Université de Bretagne Occidentale, Brest, France; CHRU de Brest, Hôpital Morvan, service de médecine physique et de réadaptation, Brest, France
| | | | - Laetitia Houx
- Laboratoire de Traitement de l'Information Médicale, INSERM U1101, Brest, France; Université de Bretagne Occidentale, Brest, France; CHRU de Brest, Hôpital Morvan, service de médecine physique et de réadaptation, Brest, France
| | - Gwenolé Quellec
- Laboratoire de Traitement de l'Information Médicale, INSERM U1101, Brest, France
| | - Sylvain Brochard
- Laboratoire de Traitement de l'Information Médicale, INSERM U1101, Brest, France; Université de Bretagne Occidentale, Brest, France; CHRU de Brest, Hôpital Morvan, service de médecine physique et de réadaptation, Brest, France
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