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Baroudi L, Barton K, Cain SM, Shorter KA. Understanding the influence of context on real-world walking energetics. J Exp Biol 2024; 227:jeb246181. [PMID: 38853583 DOI: 10.1242/jeb.246181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 05/24/2024] [Indexed: 06/11/2024]
Abstract
Speeds that minimize energetic cost during steady-state walking have been observed during lab-based investigations of walking biomechanics and energetics. However, in real-world scenarios, humans walk in a variety of contexts that can elicit different walking strategies, and may not always prioritize minimizing energetic cost. To investigate whether individuals tend to select energetically optimal speeds in real-world situations and how contextual factors influence gait, we conducted a study combining data from lab and real-world experiments. Walking kinematics and context were measured during daily life over a week (N=17) using wearable sensors and a mobile phone. To determine context, we utilized self-reported activity logs, GPS data and follow-up exit interviews. Additionally, we estimated energetic cost using respirometry over a range of gait speeds in the lab. Gross and net cost of transport were calculated for each participant, and were used to identify energetically optimal walking speed ranges for each participant. The proportion of real-world steady-state stride speeds within these ranges (gross and net) were identified for all data and for each context. We found that energetically optimal speeds predicted by gross cost of transport were more predictive of walking speeds used during daily life than speeds that would minimize net cost of transport. On average, 82.2% of all steady-state stride speeds were energetically optimal for gross cost of transport for all contexts and participants, while only 45.6% were energetically optimal for net cost of transport. These results suggest that while energetic cost is a factor considered by humans when selecting gait speed in daily life, it is not the sole determining factor. Context contributes to the observed variability in movement parameters both within and between individuals.
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Affiliation(s)
- Loubna Baroudi
- University of Michigan, Mechanical Engineering, Ann Arbor, MI 48109, USA
| | - Kira Barton
- University of Michigan, Mechanical Engineering, Ann Arbor, MI 48109, USA
- University of Michigan, Robotics, Ann Arbor, MI 48109, USA
| | - Stephen M Cain
- West Virginia University, Chemical and Biomedical Engineering, Morgantown, WV 26506, USA
| | - K Alex Shorter
- University of Michigan, Mechanical Engineering, Ann Arbor, MI 48109, USA
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2
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Rigot SK, Maronati R, Lettenberger A, O'Brien MK, Alamdari K, Hoppe-Ludwig S, McGuire M, Looft JM, Wacek A, Cave J, Sauerbrey M, Jayaraman A. Validation of Proprietary and Novel Step-counting Algorithms for Individuals Ambulating With a Lower Limb Prosthesis. Arch Phys Med Rehabil 2024; 105:546-557. [PMID: 37907160 DOI: 10.1016/j.apmr.2023.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 10/06/2023] [Accepted: 10/11/2023] [Indexed: 11/02/2023]
Abstract
OBJECTIVE To compare the accuracy and reliability of 10 different accelerometer-based step-counting algorithms for individuals with lower limb loss, accounting for different clinical characteristics and real-world activities. DESIGN Cross-sectional study. SETTING General community setting (ie, institutional research laboratory and community free-living). PARTICIPANTS Forty-eight individuals with a lower limb amputation (N=48) wore an ActiGraph (AG) wGT3x-BT accelerometer proximal to the foot of their prosthetic limb during labeled indoor/outdoor activities and community free-living. INTERVENTIONS Not applicable. MAIN OUTCOME MEASURES Intraclass correlation coefficient (ICC), absolute and root mean square error (RMSE), and Bland Altman plots were used to compare true (manual) step counts to estimated step counts from the proprietary AG Default algorithm and low frequency extension filter, as well as from 8 novel algorithms based on continuous wavelet transforms, fast Fourier transforms (FFTs), and peak detection. RESULTS All algorithms had excellent agreement with manual step counts (ICC>0.9). The AG Default and FFT algorithms had the highest overall error (RMSE=17.81 and 19.91 steps, respectively), widest limits of agreement, and highest error during outdoor and ramp ambulation. The AG Default algorithm also had among the highest error during indoor ambulation and stairs, while a FFT algorithm had the highest error during stationary tasks. Peak detection algorithms, especially those using pre-set parameters with a trial-specific component, had among the lowest error across all activities (RMSE=4.07-8.99 steps). CONCLUSIONS Because of its simplicity and accuracy across activities and clinical characteristics, we recommend the peak detection algorithm with set parameters to count steps using a prosthetic-worn AG among individuals with lower limb loss for clinical and research applications.
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Affiliation(s)
- Stephanie K Rigot
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL; Northwestern University, Department of Physical Medicine & Rehabilitation, Chicago, IL
| | - Rachel Maronati
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL
| | - Ahalya Lettenberger
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL; Rice University, Department of Bioengineering, Houston, TX
| | - Megan K O'Brien
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL; Northwestern University, Department of Physical Medicine & Rehabilitation, Chicago, IL
| | - Kayla Alamdari
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL
| | - Shenan Hoppe-Ludwig
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL
| | - Matthew McGuire
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL
| | - John M Looft
- Motion Analysis Laboratory, Department of Prosthetics, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN; Minneapolis Adaptive Design & Engineering (MADE), Department of Research, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN; Division of Rehabilitation Science, University of Minnesota Medical School, Minneapolis, MN
| | - Amber Wacek
- Motion Analysis Laboratory, Department of Prosthetics, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN; Minneapolis Adaptive Design & Engineering (MADE), Department of Research, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN
| | - Juan Cave
- Motion Analysis Laboratory, Department of Prosthetics, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN; Minneapolis Adaptive Design & Engineering (MADE), Department of Research, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN
| | - Matthew Sauerbrey
- Motion Analysis Laboratory, Department of Prosthetics, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN; Minneapolis Adaptive Design & Engineering (MADE), Department of Research, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN
| | - Arun Jayaraman
- Max Näder Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL; Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL; Northwestern University, Department of Physical Medicine & Rehabilitation, Chicago, IL; Northwestern University, Department of Physical Therapy & Human Movement Sciences, Chicago, IL.
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3
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Baroudi L, Barton K, Cain SM, Shorter KA. Classification of human walking context using a single-point accelerometer. Sci Rep 2024; 14:3039. [PMID: 38321039 PMCID: PMC10847110 DOI: 10.1038/s41598-024-53143-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 01/29/2024] [Indexed: 02/08/2024] Open
Abstract
Real-world walking data offers rich insights into a person's mobility. Yet, daily life variations can alter these patterns, making the data challenging to interpret. As such, it is essential to integrate context for the extraction of meaningful information from real-world movement data. In this work, we leveraged the relationship between the characteristics of a walking bout and context to build a classification algorithm to distinguish between indoor and outdoor walks. We used data from 20 participants wearing an accelerometer on the thigh over a week. Their walking bouts were isolated and labeled using GPS and self-reporting data. We trained and validated two machine learning models, random forest and ensemble Support Vector Machine, using a leave-one-participant-out validation scheme on 15 subjects. The 5 remaining subjects were used as a testing set to choose a final model. The chosen model achieved an accuracy of 0.941, an F1-score of 0.963, and an AUROC of 0.931. This validated model was then used to label the walks from a different dataset with 15 participants wearing the same accelerometer. Finally, we characterized the differences between indoor and outdoor walks using the ensemble of the data. We found that participants walked significantly faster, longer, and more continuously when walking outdoors compared to indoors. These results demonstrate how movement data alone can be used to obtain accurate information on important contextual factors. These factors can then be leveraged to enhance our understanding and interpretation of real-world movement data, providing deeper insights into a person's health.
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Affiliation(s)
- Loubna Baroudi
- Mechanical Engineering, University of Michigan, Ann Arbor, 48109, USA.
| | - Kira Barton
- Mechanical Engineering, University of Michigan, Ann Arbor, 48109, USA
- Robotics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Stephen M Cain
- Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, 26505, USA
| | - K Alex Shorter
- Mechanical Engineering, University of Michigan, Ann Arbor, 48109, USA
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4
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Lanotte F, O’Brien MK, Jayaraman A. AI in Rehabilitation Medicine: Opportunities and Challenges. Ann Rehabil Med 2023; 47:444-458. [PMID: 38093518 PMCID: PMC10767220 DOI: 10.5535/arm.23131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 11/23/2023] [Indexed: 01/03/2024] Open
Abstract
Artificial intelligence (AI) tools are increasingly able to learn from larger and more complex data, thus allowing clinicians and scientists to gain new insights from the information they collect about their patients every day. In rehabilitation medicine, AI can be used to find patterns in huge amounts of healthcare data. These patterns can then be leveraged at the individual level, to design personalized care strategies and interventions to optimize each patient's outcomes. However, building effective AI tools requires many careful considerations about how we collect and handle data, how we train the models, and how we interpret results. In this perspective, we discuss some of the current opportunities and challenges for AI in rehabilitation. We first review recent trends in AI for the screening, diagnosis, treatment, and continuous monitoring of disease or injury, with a special focus on the different types of healthcare data used for these applications. We then examine potential barriers to designing and integrating AI into the clinical workflow, and we propose an end-to-end framework to address these barriers and guide the development of effective AI for rehabilitation. Finally, we present ideas for future work to pave the way for AI implementation in real-world rehabilitation practices.
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Affiliation(s)
- Francesco Lanotte
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Megan K. O’Brien
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Arun Jayaraman
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
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5
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Li Z, Li Q, Huang P, Xia H, Li G. Human-in-the-Loop Adaptive Control of a Soft Exo-Suit With Actuator Dynamics and Ankle Impedance Adaptation. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:7920-7932. [PMID: 37022863 DOI: 10.1109/tcyb.2023.3240231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Soft exo-suit could facilitate walking assistance activities (such as level walking, upslope, and downslope) for unimpaired individuals. In this article, a novel human-in-the-loop adaptive control scheme is presented for a soft exo-suit, which provides ankle plantarflexion assistance with unknown human-exosuit dynamic model parameters. First, the human-exosuit coupled dynamic model is formulated to express the mathematical relationship between the exo-suit actuation system and the human ankle joint. Then, a gait detection approach, including plantarflexion assistance timing and planning, is proposed. Inspired by the control strategy that is used by the human central nervous system (CNS) to handle interaction tasks, a human-in-the-loop adaptive controller is proposed to adapt the unknown exo-suit actuator dynamics and human ankle impedance. The proposed controller can emulate human CNS behaviors which adapt feedforward force and environment impedance in interaction tasks. The resulting adaptation of actuator dynamics and ankle impedance is demonstrated with five unimpaired subjects and implemented on a developed soft exo-suit. The human-like adaptivity is performed by the exo-suit in several human walking speeds and illustrates the promising potential of the novel controller.
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6
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Hafer JF, Vitali R, Gurchiek R, Curtze C, Shull P, Cain SM. Challenges and advances in the use of wearable sensors for lower extremity biomechanics. J Biomech 2023; 157:111714. [PMID: 37423120 PMCID: PMC10529245 DOI: 10.1016/j.jbiomech.2023.111714] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/23/2023] [Accepted: 07/03/2023] [Indexed: 07/11/2023]
Abstract
The use of wearable sensors for the collection of lower extremity biomechanical data is increasing in popularity, in part due to the ease of collecting data and the ability to capture movement outside of traditional biomechanics laboratories. Consequently, an increasing number of researchers are facing the challenges that come with utilizing the data captured by wearable sensors. These challenges include identifying/calculating meaningful measures from unfamiliar data types (measures of acceleration and angular velocity instead of positions and joint angles), defining sensor-to-segment alignments for calculating traditional biomechanics metrics, using reduced sensor sets and machine learning to predict unmeasured signals, making decisions about when and how to make algorithms freely available, and developing or replicating methods to perform basic processing tasks such as recognizing activities of interest or identifying gait events. In this perspective article, we present our own approaches to common challenges in lower extremity biomechanics research using wearable sensors and share our perspectives on approaching several of these challenges. We present these perspectives with examples that come mostly from gait research, but many of the concepts also apply to other contexts where researchers may use wearable sensors. Our goal is to introduce common challenges to new users of wearable sensors, and to promote dialogue amongst experienced users towards best practices.
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Affiliation(s)
- Jocelyn F Hafer
- Department of Kinesiology and Applied Physiology, University of Delaware, Newark, DE, United States.
| | - Rachel Vitali
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, United States
| | - Reed Gurchiek
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Carolin Curtze
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, United States
| | - Peter Shull
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, China
| | - Stephen M Cain
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, United States
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7
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Hafer JF, Mihy JA, Hunt A, Zernicke RF, Johnson RT. Lower Extremity Inverse Kinematics Results Differ Between Inertial Measurement Unit- and Marker-Derived Gait Data. J Appl Biomech 2023; 39:133-142. [PMID: 37024103 DOI: 10.1123/jab.2022-0194] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 11/30/2022] [Accepted: 02/07/2023] [Indexed: 04/08/2023]
Abstract
In-lab, marker-based gait analyses may not represent real-world gait. Real-world gait analyses may be feasible using inertial measurement units (IMUs) in combination with open-source data processing pipelines (OpenSense). Before using OpenSense to study real-world gait, we must determine whether these methods estimate joint kinematics similarly to traditional marker-based motion capture (MoCap) and differentiate groups with clinically different gait mechanics. Healthy young and older adults and older adults with knee osteoarthritis completed this study. We captured MoCap and IMU data during overground walking at 2 speeds. MoCap and IMU kinematics were computed with OpenSim workflows. We tested whether sagittal kinematics differed between MoCap and IMU, whether tools detected between-group differences similarly, and whether kinematics differed between tools by speed. MoCap showed more anterior pelvic tilt (0%-100% stride) and joint flexion than IMU (hip: 0%-38% and 61%-100% stride; knee: 0%-38%, 58%-89%, and 95%-99% stride; and ankle: 6%-99% stride). There were no significant tool-by-group interactions. We found significant tool-by-speed interactions for all angles. While MoCap- and IMU-derived kinematics differed, the lack of tool-by-group interactions suggests consistent tracking across clinical cohorts. Results of the current study suggest that IMU-derived kinematics with OpenSense may enable reliable evaluation of gait in real-world settings.
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Affiliation(s)
- Jocelyn F Hafer
- Department of Kinesiology and Applied Physiology, University of Delaware, Newark, DE,USA
- School of Kinesiology, University of Michigan, Ann Arbor, MI,USA
| | - Julien A Mihy
- Department of Kinesiology and Applied Physiology, University of Delaware, Newark, DE,USA
| | - Andrew Hunt
- Department of Kinesiology and Applied Physiology, University of Delaware, Newark, DE,USA
| | - Ronald F Zernicke
- School of Kinesiology, University of Michigan, Ann Arbor, MI,USA
- Department of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI,USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI,USA
| | - Russell T Johnson
- Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, CA,USA
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8
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Yang S, Huang X, Zhou JM, Xue Q. Application of robotic lower limb orthosis for people with lower limb dysfunction. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2023; 94:041501. [PMID: 38081284 DOI: 10.1063/5.0140319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 04/03/2023] [Indexed: 12/18/2023]
Abstract
Due to the aging of the population or diseases, the number of patients with lower limb disorders has increased, causing social concern. Scholars have designed and developed advanced robotic lower limb orthoses, which can guide patients to perform reasonable rehabilitation training with correct limb postures, enhance their daily life participation and quality of life, and help them recover quickly. In recent years, a large number of new and advanced orthopedic equipment have been developed, which require a systematic summary analysis and comparison. This article reviewed typical newly developed, robotic lower limb orthoses and their use effects, as well as the advanced theories and technologies for their applications, and systematically discussed the problems in the research, design, testing, use, and popularization of robotic lower limb orthoses, and predicted their development direction in the future research and design, to enhance the reliability, convenience, and protection functions of orthotic equipment, make its functions closer to life, and give full play to the initiative of patients in the process of rehabilitation training, and reduce costs. Robotic lower limb orthoses is poised for even greater success and development in the future.
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Affiliation(s)
- Shuo Yang
- Department of Mechanical Engineering, Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry and Food Engineering Machinery and Equipment, Tianjin University of Science and Technology, Tianjin 300222, China
| | - Xin Huang
- Department of Mechanical Engineering, Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry and Food Engineering Machinery and Equipment, Tianjin University of Science and Technology, Tianjin 300222, China
| | - Jin-Man Zhou
- Department of Mechanical Engineering, Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry and Food Engineering Machinery and Equipment, Tianjin University of Science and Technology, Tianjin 300222, China
| | - Qiang Xue
- Department of Mechanical Engineering, Tianjin Key Laboratory of Integrated Design and On-line Monitoring for Light Industry and Food Engineering Machinery and Equipment, Tianjin University of Science and Technology, Tianjin 300222, China
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9
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Ng G, Andrysek J. Classifying Changes in Amputee Gait following Physiotherapy Using Machine Learning and Continuous Inertial Sensor Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:1412. [PMID: 36772451 PMCID: PMC9921298 DOI: 10.3390/s23031412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/13/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
Wearable sensors allow for the objective analysis of gait and motion both in and outside the clinical setting. However, it remains a challenge to apply such systems to highly diverse patient populations, including individuals with lower-limb amputations (LLA) that present with unique gait deviations and rehabilitation goals. This paper presents the development of a novel method using continuous gyroscope data from a single inertial sensor for person-specific classification of gait changes from a physiotherapist-led gait training session. Gyroscope data at the thigh were collected using a wearable gait analysis system for five LLA before, during, and after completing a gait training session. Data from able-bodied participants receiving no intervention were also collected. Models using dynamic time warping (DTW) and Euclidean distance in combination with the nearest neighbor classifier were applied to the gyroscope data to classify the pre- and post-training gait. The model achieved an accuracy of 98.65% ± 0.69 (Euclidean) and 98.98% ± 0.83 (DTW) on pre-training and 95.45% ± 6.20 (Euclidean) and 94.18% ± 5.77 (DTW) on post-training data across the participants whose gait changed significantly during their session. This study provides preliminary evidence that continuous angular velocity data from a single gyroscope could be used to assess changes in amputee gait. This supports future research and the development of wearable gait analysis and feedback systems that are adaptable to a broad range of mobility impairments.
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Affiliation(s)
- Gabriel Ng
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 1A1, Canada
- Bloorview Research Institute (BRI), Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON M4G 1R8, Canada
| | - Jan Andrysek
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 1A1, Canada
- Bloorview Research Institute (BRI), Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON M4G 1R8, Canada
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10
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DeGrasse NS, Mertens JC, Brzostowski JT, Allyn KJ, Vamos AC, Krout AJ, Hafner BJ, Garbini JL, Sanders JE. Beyond step counts: Including wear time in prosthesis use assessment for lower-limb amputation. J Rehabil Assist Technol Eng 2023; 10:20556683231163337. [PMID: 36935866 PMCID: PMC10021104 DOI: 10.1177/20556683231163337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023] Open
Abstract
Introduction The purpose of this study was to test a novel activity monitor that tracks the time a prosthesis is worn, and the nature of the ambulatory activity conducted with the prosthesis. These capabilities allow prosthesis users' wear and accommodation practices (e.g., temporary doffing) to be monitored, and the intensity of their activities to be assessed. Methods A portable limb-socket motion sensing system was used to monitor doffs, walk bouts (≥5 steps), low locomotion (2-4 steps), stationary positions, and weight shifts in a group of transtibial prosthesis users. The relationship between doff time and active motion time was investigated, and durations of low and high intensity active motions were compared. Results For the 14 participants tested, the median prosthesis day duration ranged from 12.8-18.8 h. Eleven participants typically doffed five or fewer times per day, and three participants typically doffed 10 or more times per day. Nine participants demonstrated a positive correlation between daily doff duration and active motion duration. Six participants spent more time in weight shifts than walk bouts, while eight participants spent more time in walk bouts than weight shifts. Conclusion Capturing don time and temporary doffs and distinguishing weight shifts from walks may provide insight relevant to patient care. Longer-term monitoring studies should be conducted, and the clinical utility of the data evaluated.
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Affiliation(s)
| | - Joseph C Mertens
- Department of Bioengineering,
University
of Washington, Seattle, WA, USA
| | | | - Katheryn J Allyn
- Department of Bioengineering,
University
of Washington, Seattle, WA, USA
| | - Andrew C Vamos
- Department of Bioengineering,
University
of Washington, Seattle, WA, USA
| | - Adam J Krout
- Department of Bioengineering,
University
of Washington, Seattle, WA, USA
| | - Brian J Hafner
- Department of Rehabilitation
Medicine, University
of Washington, Seattle, WA, USA
| | - Joseph L Garbini
- Department of Mechanical
Engineering, University
of Washington, Seattle, WA, USA
| | - Joan E Sanders
- Department of Bioengineering,
University
of Washington, Seattle, WA, USA
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11
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Finco MG, Patterson RM, Moudy SC. A pilot case series for concurrent validation of inertial measurement units to motion capture in individuals who use unilateral lower-limb prostheses. J Rehabil Assist Technol Eng 2023; 10:20556683231182322. [PMID: 37441370 PMCID: PMC10334000 DOI: 10.1177/20556683231182322] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 05/31/2023] [Indexed: 07/15/2023] Open
Abstract
Introduction Inertial measurement units (IMUs) may be viable options to collect gait data in clinics. This study compared IMU to motion capture data in individuals who use unilateral lower-limb prostheses. Methods Participants walked with lower-body IMUs and reflective markers in a motion analysis space. Sagittal plane hip, knee, and ankle waveforms were extracted for the entire gait cycle. Discrete points of peak flexion, peak extension, and range of motion were extracted from the waveforms. Stance times were also extracted to assess the IMU software's accuracy at detecting gait events. IMU and motion capture-derived data were compared using absolute differences and root mean square error (RMSE). Results Five individuals (n = 3 transtibial; n = 2 transfemoral) participated. IMU prosthetic limb data was similar to motion capture (RMSE: waveform ≤4.65°; discrete point ≤9.04°; stance ≤0.03s). However, one transfemoral participant had larger differences at the microprocessor knee joint (RMSE: waveform ≤15.64°; discrete ≤29.21°) from IMU magnetometer interference. Intact limbs tended to have minimal differences between IMU and motion capture data (RMSE: waveform ≤6.33°; discrete ≤9.87°; stance ≤0.04s). Conclusion Findings from this pilot study suggest IMUs have the potential to collect data similar to motion capture systems in sagittal plane kinematics and stance time.
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Affiliation(s)
- MG Finco
- Department of Anatomy and
Physiology, University of North Texas Health
Science Center, Fort Worth, TX, USA
| | - Rita M Patterson
- Department of Family and
Osteopathic Medicine, University of North Texas Health
Science Center, Fort Worth, TX, USA
| | - Sarah C Moudy
- Department of Anatomy and
Physiology, University of North Texas Health
Science Center, Fort Worth, TX, USA
- Department of Family and
Osteopathic Medicine, University of North Texas Health
Science Center, Fort Worth, TX, USA
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12
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Baroudi L, Yan X, Newman MW, Barton K, Cain SM, Shorter KA. Investigating walking speed variability of young adults in the real world. Gait Posture 2022; 98:69-77. [PMID: 36057208 DOI: 10.1016/j.gaitpost.2022.08.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/10/2022] [Accepted: 08/15/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Walking speed strongly correlates with health outcomes, making accurate assessment essential for clinical evaluations. However, assessments tend to be conducted over short distances, often in a laboratory or clinical setting, and may not capture natural walking behavior. To address this gap, the following questions are investigated in this work: Is walking speed significantly influenced by the continuity and duration of a walking bout? Can preferred walking speed be inferred by grouping walking bouts using duration and continuity? METHODS We collected two weeks of continuous data from fifteen healthy young adults using a thigh-worn accelerometer and a heart rate monitor. Walking strides were identified and grouped into walking periods. We quantified the duration and the continuity of each walking period. Continuity is used to parameterize changes in stepping rate related to pauses during a bout of walking. Finally, we analyzed the influence of duration and continuity on estimates of stride speed, and examined how the distribution of walking speed varies depending on different walking modes (defined by duration and continuity). RESULTS We found that continuity and duration can be used to explain some of the variability in real-world walking speed (p<0.001). Speeds estimated from long continuous walks with many strides (42% of all recorded strides) had the lowest standard deviation. Walking speed during these bouts was 1.41ms-1 (SD = 0.26ms-1). SIGNIFICANCE Walking behavior in the real world is largely variable. Features of real-world walks, like duration and continuity, can be used to explain some of the variability observed in walking speed. As such, we recommend using long continuous walks to confidently isolate the preferred walking behavior of an individual.
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Affiliation(s)
- Loubna Baroudi
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
| | - Xinghui Yan
- School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Mark W Newman
- School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Kira Barton
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Stephen M Cain
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, USA
| | - K Alex Shorter
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
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Seth M, Pohlig RT, Beisheim-Ryan EH, Stauffer SJ, Horne JR, Hicks GE, Sions JM. Residual and sound limb hip strength distinguish between sedentary and nonsedentary adults with transtibial amputation. Int J Rehabil Res 2022; 45:137-145. [PMID: 35131977 PMCID: PMC9086098 DOI: 10.1097/mrr.0000000000000520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Following a transtibial amputation (TTA), physical activity has known benefits for health and quality of life. Adults post-TTA, however, demonstrate reduced physical activity, predisposing them to adverse health outcomes. Identifying adults at the risk of sedentarism post-TTA via commonly used, objective clinical measures may enhance clinical decisions, including prosthesis prescription. The study's purpose was to determine whether residual and sound limb hip strength distinguishes between sedentary and nonsedentary adults post-TTA. A secondary analysis of a cross-sectional dataset (n = 44) was conducted. Participant residual and sound limb hip flexion, extension, abduction and adduction strength were assessed via handheld dynamometry. Physical activity was monitored for 7 days and participants were classified as sedentary (<5000 steps/day; n = 13) or nonsedentary (≥5000 steps/day; n = 31). Receiver operating curves revealed that residual and sound limb hip extension, abduction and adduction strength distinguished between sedentary and nonsedentary adults post-TTA (P < 0.050). Preliminary cut-points for hip strength measures to classify adults at the risk of sedentarism were determined. A hip strength composite score (0-6) estimates a 2.2× increased odds of being sedentary with each additional hip strength deficit. Post-TTA, residual and sound limb hip strength can help identify adults at risk of sedentarism to aid clinical decision making, including prosthesis prescription.
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Affiliation(s)
- Mayank Seth
- Delaware Limb Loss Studies, Department of Physical Therapy, University of Delaware, Newark DE, USA
| | - Ryan T Pohlig
- Biostatistics Core, University of Delaware, Newark, DE, USA
| | - Emma H Beisheim-Ryan
- Delaware Limb Loss Studies, Department of Physical Therapy, University of Delaware, Newark DE, USA
| | - Samantha J Stauffer
- Delaware Limb Loss Studies, Department of Physical Therapy, University of Delaware, Newark DE, USA
- Independence Prosthetics-Orthotics, Inc., Newark, DE, USA
| | - John R Horne
- Independence Prosthetics-Orthotics, Inc., Newark, DE, USA
| | - Gregory E Hicks
- Delaware Spine Studies, Department of Physical Therapy, University of Delaware, Newark DE, USA
| | - Jaclyn Megan Sions
- Delaware Limb Loss Studies, Department of Physical Therapy, University of Delaware, Newark DE, USA
- Delaware Spine Studies, Department of Physical Therapy, University of Delaware, Newark DE, USA
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Mellema M, Gjøvaag T. Reported Outcome Measures in Studies of Real-World Ambulation in People with a Lower Limb Amputation: A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:2243. [PMID: 35336412 PMCID: PMC8955603 DOI: 10.3390/s22062243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND The rapidly increasing use of wearable technology to monitor free-living ambulatory behavior demands to address to what extent the chosen outcome measures are representative for real-world situations. This scoping review aims to provide an overview of the purpose of use of wearable activity monitors in people with a Lower Limb Amputation (LLA) in the real world, to identify the reported outcome measures, and to evaluate to what extent the reported outcome measures capture essential information from real-world ambulation of people with LLA. METHODS The literature search included a search in three databases (MEDLINE, CINAHL, and EMBASE) for articles published between January 1999 and January 2022, and a hand-search. RESULTS AND CONCLUSIONS 98 articles met the inclusion criteria. According to the included studies' main objective, the articles were classified into observational (n = 46), interventional (n = 34), algorithm/method development (n = 12), and validity/feasibility studies (n = 6). Reported outcome measures were grouped into eight categories: step count (reported in 73% of the articles), intensity of activity/fitness (31%), type of activity/body posture (27%), commercial scores (15%), prosthetic use and fit (11%), gait quality (7%), GPS (5%), and accuracy (4%). We argue that researchers should be more careful with choosing reliable outcome measures, in particular, regarding the frequently used category step count. However, the contemporary technology is limited in providing a comprehensive picture of real-world ambulation. The novel knowledge from this review should encourage researchers and developers to engage in debating and defining the framework of ecological validity in rehabilitation sciences, and how this framework can be utilized in the development of wearable technologies and future studies of real-world ambulation in people with LLA.
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Affiliation(s)
- Mirjam Mellema
- Department of Mechanical, Electronic and Chemical Engineering, Faculty of Technology, Art and Design, Oslo Metropolitan University, P.O. Box 4, St. Olavs Plass, 0130 Oslo, Norway
- Department of Occupational Therapy, Prosthetics and Orthotics, Faculty of Health Sciences, Oslo Metropolitan University, P.O. Box 4, St. Olavs Plass, 0130 Oslo, Norway;
| | - Terje Gjøvaag
- Department of Occupational Therapy, Prosthetics and Orthotics, Faculty of Health Sciences, Oslo Metropolitan University, P.O. Box 4, St. Olavs Plass, 0130 Oslo, Norway;
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Picerno P, Iosa M, D'Souza C, Benedetti MG, Paolucci S, Morone G. Wearable inertial sensors for human movement analysis: a five-year update. Expert Rev Med Devices 2021; 18:79-94. [PMID: 34601995 DOI: 10.1080/17434440.2021.1988849] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
INTRODUCTION The aim of the present review is to track the evolution of wearable IMUs from their use in supervised laboratory- and ambulatory-based settings to their application for long-term monitoring of human movement in unsupervised naturalistic settings. AREAS COVERED Four main emerging areas of application were identified and synthesized, namely, mobile health solutions (specifically, for the assessment of frailty, risk of falls, chronic neurological diseases, and for the monitoring and promotion of active living), occupational ergonomics, rehabilitation and telerehabilitation, and cognitive assessment. Findings from recent scientific literature in each of these areas was synthesized from an applied and/or clinical perspective with the purpose of providing clinical researchers and practitioners with practical guidance on contemporary uses of inertial sensors in applied clinical settings. EXPERT OPINION IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings. Successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely also on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners.
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Affiliation(s)
- Pietro Picerno
- SMART Engineering Solutions & Technologies (SMARTEST) Research Center, Università Telematica "Ecampus", Novedrate, Comune, Italy
| | - Marco Iosa
- Department of Psychology, Sapienza University, Rome, Italy.,Irrcs Santa Lucia Foundation, Rome, Italy
| | - Clive D'Souza
- Center for Ergonomics, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan, USA.,Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Maria Grazia Benedetti
- Physical Medicine and Rehabilitation Unit, IRCCS-Istituto Ortopedico Rizzoli, Bologna, Italy
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The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review. SENSORS 2021; 21:s21144808. [PMID: 34300546 PMCID: PMC8309920 DOI: 10.3390/s21144808] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/05/2021] [Accepted: 07/08/2021] [Indexed: 12/28/2022]
Abstract
Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.
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Mallat R, Bonnet V, Dumas R, Adjel M, Venture G, Khalil M, Mohammed S. Sparse Visual-Inertial Measurement Units Placement for Gait Kinematics Assessment. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1300-1311. [PMID: 34138711 DOI: 10.1109/tnsre.2021.3089873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This study investigates the possibility of estimating lower-limb joint kinematics and meaningful performance indexes for physiotherapists, during gait on a treadmill based on data collected from a sparse placement of new Visual Inertial Measurement Units (VIMU) and the use of an Extended Kalman Filter (EKF). The proposed EKF takes advantage of the biomechanics of the human body and of the investigated task to reduce sensor inaccuracies. Two state-vector formulations, one based on the use of constant acceleration model and one based on Fourier series, and the tuning of their corresponding parameters were analyzed. The constant acceleration model, due to its inherent inconsistency for human motion, required a cumbersome optimisation process and needed the a-priori knowledge of reference joint trajectories for EKF parameters tuning. On the other hand, the Fourier series formulation could be used without a specific parameters tuning process. In both cases, the average root mean square difference and correlation coefficient between the estimated joint angles and those reconstructed with a reference stereophotogrammetric system was 3.5deg and 0.70, respectively. Moreover, the stride lengths were estimated with a normalized root mean square difference inferior to 2% when using the forward kinematics model receiving as input the estimated joint angles. The popular gait deviation index was also estimated and showed similar results very close to 100, using both the proposed method and the reference stereophotogrammetric system. Such consistency was obtained using only three wireless and affordable VIMU located at the pelvis and both heels and tracked using two affordable RGB cameras. Being further easy-to-use and suitable for applications taking place outside of the laboratory, the proposed method thus represents a good compromise between accurate reference stereophotogrammetric systems and markerless ones for which accuracy is still under debate.
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Kim J, Wensman J, Colabianchi N, Gates DH. The influence of powered prostheses on user perspectives, metabolics, and activity: a randomized crossover trial. J Neuroeng Rehabil 2021; 18:49. [PMID: 33726802 PMCID: PMC7962267 DOI: 10.1186/s12984-021-00842-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 02/26/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Powered prosthetic ankles provide battery-powered mechanical push-off, with the aim of reducing the metabolic demands of walking for people with transtibial amputations. The efficacy of powered ankles has been shown in active, high functioning individuals with transtibial amputation, but is less clear in other populations. Additionally, it is unclear how use of a powered prosthesis influences everyday physical activity and mobility. METHODS Individuals with unilateral transtibial amputations participated in a randomized clinical trial comparing their prescribed, unpowered prosthesis and the BiOM powered prosthesis. Participants' metabolic costs and self-selected walking speeds were measured in the laboratory and daily step count, daily steps away from home, and walking speed were measured over two weeks of at-home prosthesis use. Participants also rated their perception of mobility and quality of life and provided free-form feedback. Dependent measures were compared between prostheses and the relationships between metabolic cost, perception of mobility, and characteristics of walking in daily life were explored using Pearson's correlations. RESULTS Twelve people were randomly allocated to the powered prosthesis first (n = 7) or unpowered prosthesis first (n = 5) and ten completed the full study. There were no differences in metabolic costs (p = 0.585), daily step count (p = 0.995), walking speed in-lab (p = 0.145) and in daily life (p = 0.226), or perception of mobility between prostheses (p ≥ 0.058). Changes varied across participants, however. There were several medium-sized effects for device comparisons. With the powered prosthesis, participants had increased self-reported ambulation (g = 0.682) and decreased frustration (g = 0.506). CONCLUSIONS There were no universal benefits of the powered prosthesis on function in the lab or home environment. However, the effects were subject-specific, with some reporting preference for power and improved mobility, and some increasing their activity and decreasing their metabolic effort. Additionally, self-reported preferences did not often correlate with objective measures of function. This highlights the need for future clinical research to include both perception and objective measures to better inform prosthetic prescription. TRIAL REGISTRATION https://clinicaltrials.gov , #NCT02828982. Registered 12 July 2016, https://clinicaltrials.gov/ct2/show/NCT02828982.
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Affiliation(s)
- Jay Kim
- School of Kinesiology, University of Michigan, 401 Washtenaw Avenue, Ann Arbor, MI, 48109-2214, USA.,Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jeffrey Wensman
- University of Michigan Orthotics and Prosthetics Center, Ann Arbor, MI, USA
| | - Natalie Colabianchi
- School of Kinesiology, University of Michigan, 401 Washtenaw Avenue, Ann Arbor, MI, 48109-2214, USA
| | - Deanna H Gates
- School of Kinesiology, University of Michigan, 401 Washtenaw Avenue, Ann Arbor, MI, 48109-2214, USA. .,Department of Biomedical Engineering, University of Michigan, 401 Washtenaw Avenue, Ann Arbor, MI, 48109-2214, USA.
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