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Young F, Mason R, Morris RE, Stuart S, Godfrey A. IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:4100. [PMID: 37112441 PMCID: PMC10144082 DOI: 10.3390/s23084100] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 06/19/2023]
Abstract
Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation that has spawned the highest growth in instrumented gait assessment due to the capabilities for monitoring within and beyond the laboratory. Specifically, instrumented gait assessment with wearable inertial measurement units (IMUs) has provided more readily deployable devices for use in any environment. Contemporary IMU-based gait assessment research has shown evidence of the robust quantifying of important clinical gait outcomes in, e.g., neurological disorders to gather more insightful habitual data in the home and community, given the relatively low cost and portability of IMUs. The aim of this narrative review is to describe the ongoing research regarding the need to move gait assessment out of bespoke settings into habitual environments and to consider the shortcomings and inefficiencies that are common within the field. Accordingly, we broadly explore how the Internet of Things (IoT) could better enable routine gait assessment beyond bespoke settings. As IMU-based wearables and algorithms mature in their corroboration with alternate technologies, such as computer vision, edge computing, and pose estimation, the role of IoT communication will enable new opportunities for remote gait assessment.
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Affiliation(s)
- Fraser Young
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rachel Mason
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rosie E. Morris
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Samuel Stuart
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
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Wei L, Chung CS, Koontz AM. Automating the Clinical Assessment of Independent Wheelchair Sitting Pivot Transfer Techniques. Top Spinal Cord Inj Rehabil 2021; 27:1-11. [PMID: 34456542 DOI: 10.46292/sci20-00050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Background Using proper transfer technique can help to reduce forces and prevent secondary injuries. However, current assessment tools rely on the ability to subjectively identify harmful movement patterns. Objectives The purpose of the study was to determine the accuracy of using a low-cost markerless motion capture camera and machine learning methods to evaluate the quality of independent wheelchair sitting pivot transfers. We hypothesized that the algorithms would be able to discern proper (low risk) and improper (high risk) wheelchair transfer techniques in accordance with component items on the Transfer Assessment Instrument (TAI). Methods Transfer motions of 91 full-time wheelchair users were recorded and used to develop machine learning classifiers that could be used to discern proper from improper technique. The data were labeled using the TAI item scores. Eleven out of 18 TAI items were evaluated by the classifiers. Motion variables from the Kinect were inputted as the features. Random forests and k-nearest neighbors algorithms were chosen as the classifiers. Eighty percent of the data were used for model training and hyperparameter turning. The validation process was performed using 20% of the data as the test set. Results The area under the receiver operating characteristic curve of the test set for each item was over 0.79. After adjusting the decision threshold, the precisions of the models were over 0.87, and the model accuracies were over 71%. Conclusion The results show promise for the objective assessment of the transfer technique using a low cost camera and machine learning classifiers.
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Affiliation(s)
- Lin Wei
- Human Engineering Research Laboratories, Rehabilitation Research and Development Service, Department of Veterans Affairs, Pittsburgh, PA.,Department of Rehabilitation Science and Technology, University of Pittsburgh, PA
| | - Cheng-Shiu Chung
- Human Engineering Research Laboratories, Rehabilitation Research and Development Service, Department of Veterans Affairs, Pittsburgh, PA
| | - Alicia M Koontz
- Human Engineering Research Laboratories, Rehabilitation Research and Development Service, Department of Veterans Affairs, Pittsburgh, PA.,Department of Rehabilitation Science and Technology, University of Pittsburgh, PA.,Department of Bioengineering, University of Pittsburgh, PA
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The efficacy of the Microsoft KinectTM to assess human bimanual coordination. Behav Res Methods 2016; 49:1030-1047. [DOI: 10.3758/s13428-016-0764-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Baldewijns G, Luca S, Vanrumste B, Croonenborghs T. Developing a system that can automatically detect health changes using transfer times of older adults. BMC Med Res Methodol 2016; 16:23. [PMID: 26897003 PMCID: PMC4761129 DOI: 10.1186/s12874-016-0124-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Accepted: 02/10/2016] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND As gait speed and transfer times are considered to be an important measure of functional ability in older adults, several systems are currently being researched to measure this parameter in the home environment of older adults. The data resulting from these systems, however, still needs to be reviewed by healthcare workers which is a time-consuming process. METHODS This paper presents a system that employs statistical process control techniques (SPC) to automatically detect both positive and negative trends in transfer times. Several SPC techniques, Tabular cumulative sum (CUSUM) chart, Standardized CUSUM and Exponentially Weighted Moving Average (EWMA) chart were evaluated. The best performing method was further optimized for the desired application. After this, it was validated on both simulated data and real-life data. RESULTS The best performing method was the Exponentially Weighted Moving Average control chart with the use of rational subgroups and a reinitialization after three alarm days. The results from the simulated data showed that positive and negative trends are detected within 14 days after the start of the trend when a trend is 28 days long. When the transition period is shorter, the number of days before an alert is triggered also diminishes. If for instance an abrupt change is present in the transfer time an alert is triggered within two days after this change. On average, only one false alarm is triggered every five weeks. The results from the real-life dataset confirm those of the simulated dataset. CONCLUSIONS The system presented in this paper is able to detect both positive and negative trends in the transfer times of older adults, therefore automatically triggering an alarm when changes in transfer times occur. These changes can be gradual as well as abrupt.
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Affiliation(s)
- Greet Baldewijns
- KU Leuven Technology Campus Geel, AdvISe, Kleinhoefstraat 4, Geel, Belgium.
- KU Leuven, ESAT-STADIUS,, Leuven, Belgium.
- iMinds Medical Information Technology Department, Leuven, Belgium.
| | - Stijn Luca
- KU Leuven Technology Campus Geel, AdvISe, Kleinhoefstraat 4, Geel, Belgium
- KU Leuven, ESAT-STADIUS,, Leuven, Belgium
| | - Bart Vanrumste
- KU Leuven Technology Campus Geel, AdvISe, Kleinhoefstraat 4, Geel, Belgium
- KU Leuven, ESAT-STADIUS,, Leuven, Belgium
- iMinds Medical Information Technology Department, Leuven, Belgium
| | - Tom Croonenborghs
- KU Leuven Technology Campus Geel, AdvISe, Kleinhoefstraat 4, Geel, Belgium
- KU Leuven, Department of Computer Science, DTAI, Leuven, Belgium
- Program in Translational NeuroPsychiatric Genomics, Brigham and Women's Hospital, Harvard Medical School, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
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A Review on Technical and Clinical Impact of Microsoft Kinect on Physical Therapy and Rehabilitation. J Med Eng 2014; 2014:846514. [PMID: 27006935 PMCID: PMC4782741 DOI: 10.1155/2014/846514] [Citation(s) in RCA: 164] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2014] [Revised: 11/03/2014] [Accepted: 11/17/2014] [Indexed: 01/12/2023] Open
Abstract
This paper reviews technical and clinical impact of the Microsoft Kinect in physical therapy and rehabilitation. It covers the studies on patients with neurological disorders including stroke, Parkinson's, cerebral palsy, and MS as well as the elderly patients. Search results in Pubmed and Google scholar reveal increasing interest in using Kinect in medical application. Relevant papers are reviewed and divided into three groups: (1) papers which evaluated Kinect's accuracy and reliability, (2) papers which used Kinect for a rehabilitation system and provided clinical evaluation involving patients, and (3) papers which proposed a Kinect-based system for rehabilitation but fell short of providing clinical validation. At last, to serve as technical comparison to help future rehabilitation design other sensors similar to Kinect are reviewed.
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Reeder B, Whitehouse K. Sensor-based detection of gait speed in older adults: an integrative review. Res Gerontol Nurs 2014; 8:12-27. [PMID: 25420184 DOI: 10.3928/19404921-20141120-02] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2014] [Accepted: 08/20/2014] [Indexed: 11/20/2022]
Abstract
Gait speed is an indicator of functional change in older adults. One approach to support older adults' preferences to "age in place" is through the use of technology to monitor gait speed in everyday life. The authors of the current article conducted an integrative review of the scientific literature to summarize the current state of gait speed detection technologies. A total of 539 articles were returned from searches, and 16 were included in the review. Technologies were categorized as body-worn or home-installed sensors. Evidence was classified as emerging (n = 8) or promising (n = 8). Gait speed technology research has advanced beyond the use of customized research hardware prototypes, and consumer technologies are now commonly used in gait speed research. However, a need exists for software systems that integrate data for analysis and presentation to stakeholders with different information needs. Future research should focus on approaches to integrate disparate data sources and visualizations of gait speed data.
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Behrens J, Pfüller C, Mansow-Model S, Otte K, Paul F, Brandt AU. Using perceptive computing in multiple sclerosis - the Short Maximum Speed Walk test. J Neuroeng Rehabil 2014; 11:89. [PMID: 24886525 PMCID: PMC4041903 DOI: 10.1186/1743-0003-11-89] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2013] [Accepted: 05/22/2014] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND We investigated the applicability and feasibility of perceptive computing assisted gait analysis in multiple sclerosis (MS) patients using Microsoft Kinect™. To detect the maximum walking speed and the degree of spatial sway, we established a computerized and observer-independent measure, which we named Short Maximum Speed Walk (SMSW), and compared it to established clinical measures of gait disability in MS, namely the Expanded Disability Status Scale (EDSS) and the Timed 25-Foot Walk (T25FW). METHODS Cross-sectional study of 22 MS patients (age mean ± SD 43 ± 9 years, 13 female) and 22 age and gender matched healthy control subjects (HC) (age 37 ± 11 years, 13 female). The disability level of each MS patient was graded using the EDSS (median 3.0, range 0.0-6.0). All subjects then performed the SMSW and the Timed 25-Foot Walk (T25FW). The SMSW comprised five gait parameters, which together assessed average walking speed and gait stability in different dimensions (left/right, up/down and 3D deviation). RESULTS SMSW average walking speed was slower in MS patients (1.6 ± 0.3 m/sec) than in HC (1.8 ± 0.4 m/sec) (p = 0.005) and correlated well with EDSS (Spearman's Rho 0.676, p < 0.001). Furthermore, SMSW revealed higher left/right deviation in MS patients compared to HC. SMSW showed high recognition quality and retest-reliability (covariance 0.13 m/sec, ICC 0.965, p < 0.001). There was a significant correlation between SMSW average walking speed and T25FW (Pearson's R = -0.447, p = 0.042). CONCLUSION Our data suggest that ambulation tests using Microsoft Kinect™ are feasible, well tolerated and can detect clinical gait disturbances in patients with MS. The retest-reliability was on par with the T25FW.
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Affiliation(s)
| | | | | | | | - Friedemann Paul
- NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
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Iarlori S, Ferracuti F, Giantomassi A, Longhi S. RGBD camera monitoring system for Alzheimer's disease assessment using Recurrent Neural Networks with Parametric Bias action recognition. ACTA ACUST UNITED AC 2014. [DOI: 10.3182/20140824-6-za-1003.02199] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Li Y, Ho KC, Popescu M. Efficient source separation algorithms for acoustic fall detection using a microsoft kinect. IEEE Trans Biomed Eng 2013; 61:745-55. [PMID: 24235295 DOI: 10.1109/tbme.2013.2288783] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Falls have become a common health problem among older adults. In previous study, we proposed an acoustic fall detection system (acoustic FADE) that employed a microphone array and beamforming to provide automatic fall detection. However, the previous acoustic FADE had difficulties in detecting the fall signal in environments where interference comes from the fall direction, the number of interferences exceeds FADE's ability to handle or a fall is occluded. To address these issues, in this paper, we propose two blind source separation (BSS) methods for extracting the fall signal out of the interferences to improve the fall classification task. We first propose the single-channel BSS by using nonnegative matrix factorization (NMF) to automatically decompose the mixture into a linear combination of several basis components. Based on the distinct patterns of the bases of falls, we identify them efficiently and then construct the interference free fall signal. Next, we extend the single-channel BSS to the multichannel case through a joint NMF over all channels followed by a delay-and-sum beamformer for additional ambient noise reduction. In our experiments, we used the Microsoft Kinect to collect the acoustic data in real-home environments. The results show that in environments with high interference and background noise levels, the fall detection performance is significantly improved using the proposed BSS approaches.
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Li Y, Banerjee T, Popescu M, Skubic M. Improvement of acoustic fall detection using Kinect depth sensing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:6736-9. [PMID: 24111289 DOI: 10.1109/embc.2013.6611102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The latest acoustic fall detection system (acoustic FADE) has achieved encouraging results on real-world dataset. However, the acoustic FADE device is difficult to be deployed in real environment due to its large size. In addition, the estimation accuracy of sound source localization (SSL) and direction of arrival (DOA) becomes much lower in multi-interference environment, which will potentially result in the distortion of the source signal using beamforming (BF). Microsoft Kinect is used in this paper to address these issues by measuring source position using the depth sensor. We employ robust minimum variance distortionless response (MVDR) adaptive BF (ABF) to take advantage of well-estimated source position for acoustic FADE. A significant reduction of false alarms and improvement of detection rate are both achieved using the proposed fusion strategy on real-world data.
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Wallace R, Abbott C, Gibson-Horn C, Skubic M. In-home measurement of the effect of strategically weighted vests on ambulation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:949-52. [PMID: 24109846 DOI: 10.1109/embc.2013.6609659] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Strategically weighted vests are currently being used to treat patients with Parkinson's, Multiple Sclerosis, and ataxia. While studies have been conducted to demonstrate the effectiveness of these vests, there has been very little research into the mechanisms that give rise to the vest's results. This study demonstrates the ability to capture gait parameters from depth images[1] in the home with sufficient sensitivity to support future investigation of the weighted vest intervention. The study also explores multiple metrics, using in-home gait sensing, to study a subject's ambulatory ability including gait mechanics, uncertainty in motion, and gait cadence. We then investigate the effects of these vests on a subject's ambulation by examining these metrics both before and after the vest is worn. While only four subjects were used, results are promising, showing a statistically significant and clinically significant change in many of these metrics as a result of the vest. The cases presented here concern two subjects, one with a "tight" gait caused by Progressive Supranuclear Palsy, and the second with an excessively "loose" gait due to Parkinson's disease. We show that in both subjects, using the vest immediately moved the metrics in a direction beneficial to the subject's clinical condition. This result concurs with clinical observations as measured using various clinical fall risk instruments.
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Stone EE, Skubic M. Unobtrusive, continuous, in-home gait measurement using the Microsoft Kinect. IEEE Trans Biomed Eng 2013; 60:2925-32. [PMID: 23744661 DOI: 10.1109/tbme.2013.2266341] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A system for capturing habitual, in-home gait measurements using an environmentally mounted depth camera, the Microsoft Kinect, is presented. Previous work evaluating the use of the Kinect sensor for in-home gait measurement in a lab setting has shown the potential of this approach. In this paper, a single Kinect sensor and computer were deployed in the apartments of older adults in an independent living facility for the purpose of continuous, in-home gait measurement. In addition, a monthly fall risk assessment protocol was conducted for each resident by a clinician, which included traditional tools such as the timed up a go and habitual gait speed tests. A probabilistic methodology for generating automated gait estimates over time for the residents of the apartments from the Kinect data is described, along with results from the apartments as compared to two of the traditionally measured fall risk assessment tools. Potential applications and future work are discussed.
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