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Bianchini E, Caliò B, Alborghetti M, Rinaldi D, Hansen C, Vuillerme N, Maetzler W, Pontieri FE. Step-Counting Accuracy of a Commercial Smartwatch in Mild-to-Moderate PD Patients and Effect of Spatiotemporal Gait Parameters, Laterality of Symptoms, Pharmacological State, and Clinical Variables. SENSORS (BASEL, SWITZERLAND) 2022; 23:214. [PMID: 36616812 PMCID: PMC9823757 DOI: 10.3390/s23010214] [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: 12/06/2022] [Revised: 12/20/2022] [Accepted: 12/22/2022] [Indexed: 06/17/2023]
Abstract
Commercial smartwatches could be useful for step counting and monitoring ambulatory activity. However, in Parkinson's disease (PD) patients, an altered gait, pharmacological condition, and symptoms lateralization may affect their accuracy and potential usefulness in research and clinical routine. Steps were counted during a 6 min walk in 47 patients with PD and 47 healthy subjects (HS) wearing a Garmin Vivosmart 4 (GV4) on each wrist. Manual step counting was used as a reference. An inertial sensor (BTS G-Walk), placed on the lower back, was used to compute spatial-temporal gait parameters. Intraclass correlation coefficient (ICC) and mean absolute percentage error (MAPE) were used for accuracy evaluation and the Spearman test was used to assess the correlations between variables. The GV4 overestimated steps in PD patients with only a poor-to-moderate agreement. The OFF pharmacological state and wearing the device on the most-affected body side led to an unacceptable accuracy. The GV4 showed an excellent agreement and MAPE in HS at a self-selected speed, but an unacceptable performance at a slow speed. In PD patients, MAPE was not associated with gait parameters and clinical variables. The accuracy of commercial smartwatches for monitoring step counting might be reduced in PD patients and further influenced by the pharmacological condition and placement of the device.
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Affiliation(s)
- Edoardo Bianchini
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Sapienza University of Rome, 00189 Rome, Italy
| | - Bianca Caliò
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Sapienza University of Rome, 00189 Rome, Italy
| | - Marika Alborghetti
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Sapienza University of Rome, 00189 Rome, Italy
| | - Domiziana Rinaldi
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Sapienza University of Rome, 00189 Rome, Italy
- Santa Lucia Foundation, IRCCS, 00179 Rome, Italy
| | - Clint Hansen
- Department of Neurology, Kiel University, 24105 Kiel, Germany
| | - Nicolas Vuillerme
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France
- LabCom Telecom4Health, Orange Labs & Université Grenoble Alpes, CNRS, Inria, Grenoble INP-UGA, 38000 Grenoble, France
- Institut Universitaire de France, 75005 Paris, France
| | - Walter Maetzler
- Department of Neurology, Kiel University, 24105 Kiel, Germany
| | - Francesco E. Pontieri
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Sapienza University of Rome, 00189 Rome, Italy
- Santa Lucia Foundation, IRCCS, 00179 Rome, Italy
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Furtado S, Godfrey A, Del Din S, Rochester L, Gerrand C. Free-living monitoring of ambulatory activity after treatments for lower extremity musculoskeletal cancers using an accelerometer-based wearable - a new paradigm to outcome assessment in musculoskeletal oncology? Disabil Rehabil 2022:1-10. [PMID: 35710327 DOI: 10.1080/09638288.2022.2083701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
PURPOSE Ambulatory activity (walking) is affected after sarcoma surgery yet is not routinely assessed. Small inexpensive accelerometers could bridge the gap. Study objectives investigated, whether in patients with lower extremity musculoskeletal tumours: (A) it was feasible to conduct ambulatory activity assessments in patient's homes using an accelerometer-based wearable (AX3, Axivity). (B) AX3 assessments produced clinically useful data, distinguished tumour sub-groups and related to existing measures. METHODS In a prospective cross-sectional pilot, 34 patients with musculoskeletal tumours in the femur/thigh (19), pelvis/hip (3), tibia/leg (9), or ankle/foot (3) participated. Twenty-seven had limb-sparing surgery and seven amputation. Patients were assessed using a thigh-worn monitor. Summary measures of volume (total steps/day, total ambulatory bouts/day, mean bout length), pattern (alpha), and variability (S2) of ambulatory activity were derived. RESULTS AX3 was well-tolerated and feasible to use. Outcomes compared to literature but did not distinguish tumour sub-groups. Alpha negatively correlated with disability (walking outside (r=-418, p = 0.042*), social life (r=-0.512, p = 0.010*)). Disability negatively predicted alpha (unstandardised co-efficient= -0.001, R2=0.186, p = 0.039*). CONCLUSIONS A wearable can assess novel attributes of walking; volume, pattern, and variability after sarcoma surgery. Such outcomes provide valuable information about people's physical performance in their homes, which can guide rehabilitation. Implications for rehabilitationRoutine capture of ambulatory activity by sarcoma services in peoples' homes can provide important information about individuals "actual" physical activity levels and limitations after sarcoma surgery to inform personalised rehabilitation and care needs, including timely referral for support.Routine remote ambulatory monitoring about out of hospital activity can support personalised care for patients, including identifying high risk patients who need rapid intervention and care closer to home.Use of routine remote ambulatory monitoring could enhance delivery of evidence-based care closer to peoples' homes without disrupting their daily routine and therefore reducing patient and carer burden.Collection of data close to home using questionnaires and objective community assessment could be more cost effective and comprehensive than in-hospital assessment and could reduce the need for hospital attendance, which is of importance to vulnerable patients, particularly during the Covid-19 pandemic.
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Affiliation(s)
- Sherron Furtado
- The London Sarcoma Service, Royal National Orthopaedic Hospital NHS Trust, Stanmore, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Silvia Del Din
- Faculty of Medical Sciences, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Lynn Rochester
- Faculty of Medical Sciences, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.,Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Craig Gerrand
- The London Sarcoma Service, Royal National Orthopaedic Hospital NHS Trust, Stanmore, UK
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McDonald S, Tan SX, Banu S, van Driel M, McGree JM, Mitchell G, Nikles J. Exploring Symptom Fluctuations and Triggers in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Novel Patient-Centred N-of-1 Observational Designs: A Protocol for a Feasibility and Acceptability Study. PATIENT-PATIENT CENTERED OUTCOMES RESEARCH 2021; 15:197-206. [PMID: 34368926 DOI: 10.1007/s40271-021-00540-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/15/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic condition of unknown aetiology associated with a range of disabling symptoms, including post-exertional malaise, chronic fatigue, musculoskeletal pain, orthostatic intolerance, unrefreshing sleep, and cognitive dysfunction. ME/CFS is a heterogeneous disorder, with significant variation in symptom type and severity between individuals, as well as within individuals over time. The diversity of ME/CFS symptom presentation makes management challenging; treatments supported by data from randomised controlled trials may not work for all individuals due to the variability in experienced symptoms. Studies using quantitative N-of-1 observational designs involve repeated outcome measurements in an individual over time and can generate rigorous individual-specific conclusions about symptom patterns and triggers in individuals with ME/CFS. This study aims to explore the feasibility and acceptability of using novel patient-centred N-of-1 observational designs to explore symptom fluctuations and triggers in ME/CFS at the individual level. METHODS AND ANALYSIS Individuals with a medical diagnosis of ME/CFS will be recruited through ME/CFS patient organisations to participate in a series of patient-centred N-of-1 observational studies. Using a wrist-worn electronic diary, participants will complete ecological momentary assessments of fatigue, stress, mood, and cognitive demand, three times per day for a period of 6-12 weeks. Personally relevant symptoms and triggers will also be incorporated into the questionnaire design. Physical activity will be objectively measured via an integrated accelerometer. Feasibility and acceptability outcomes will be assessed including the percentage of diary entries completed, as well as recruitment and retention rate, feasibility of analysing and interpreting the data collected, and participant views about participation elicited via a post-study semi-structured interview. DISCUSSION This study will assess the feasibility and acceptability of patient-centred N-of-1 observational studies to assess diseases with complex presentations such as ME/CFS, as well as provide individual-level evidence about fluctuations and triggers of ME/CFS symptoms that may aid self-management. TRIAL REGISTRATION Australian and New Zealand Clinical Trials Registry: ACTRN12618001898246. Registered on 22 November 2018.
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Affiliation(s)
- Suzanne McDonald
- Centre for Clinical Research, The University of Queensland, Brisbane, QLD, Australia.
| | - Samuel X Tan
- Centre for Clinical Research, The University of Queensland, Brisbane, QLD, Australia
| | - Shamima Banu
- Centre for Clinical Research, The University of Queensland, Brisbane, QLD, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Mieke van Driel
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - James M McGree
- Science Faculty, Queensland University of Technology, Brisbane, QLD, Australia
| | - Geoffrey Mitchell
- Centre for Clinical Research, The University of Queensland, Brisbane, QLD, Australia.,Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Jane Nikles
- Centre for Clinical Research, The University of Queensland, Brisbane, QLD, Australia
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Powell D, Celik Y, Trojaniello D, Young F, Moore J, Stuart S, Godfrey A. Instrumenting traditional approaches to physical assessment. Digit Health 2021. [DOI: 10.1016/b978-0-12-818914-6.00005-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Rast FM, Labruyère R. Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. J Neuroeng Rehabil 2020; 17:148. [PMID: 33148315 PMCID: PMC7640711 DOI: 10.1186/s12984-020-00779-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 10/22/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Recent advances in wearable sensor technologies enable objective and long-term monitoring of motor activities in a patient's habitual environment. People with mobility impairments require appropriate data processing algorithms that deal with their altered movement patterns and determine clinically meaningful outcome measures. Over the years, a large variety of algorithms have been published and this review provides an overview of their outcome measures, the concepts of the algorithms, the type and placement of required sensors as well as the investigated patient populations and measurement properties. METHODS A systematic search was conducted in MEDLINE, EMBASE, and SCOPUS in October 2019. The search strategy was designed to identify studies that (1) involved people with mobility impairments, (2) used wearable inertial sensors, (3) provided a description of the underlying algorithm, and (4) quantified an aspect of everyday life motor activity. The two review authors independently screened the search hits for eligibility and conducted the data extraction for the narrative review. RESULTS Ninety-five studies were included in this review. They covered a large variety of outcome measures and algorithms which can be grouped into four categories: (1) maintaining and changing a body position, (2) walking and moving, (3) moving around using a wheelchair, and (4) activities that involve the upper extremity. The validity or reproducibility of these outcomes measures was investigated in fourteen different patient populations. Most of the studies evaluated the algorithm's accuracy to detect certain activities in unlabeled raw data. The type and placement of required sensor technologies depends on the activity and outcome measure and are thoroughly described in this review. The usability of the applied sensor setups was rarely reported. CONCLUSION This systematic review provides a comprehensive overview of applications of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. It summarizes the state-of-the-art, it provides quick access to the relevant literature, and it enables the identification of gaps for the evaluation of existing and the development of new algorithms.
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Affiliation(s)
- Fabian Marcel Rast
- Swiss Children’s Rehab, University Children’s Hospital Zurich, Mühlebergstrasse 104, 8910 Affoltern am Albis, Switzerland
- Children’s Research Center, University Children’s Hospital of Zurich, University of Zurich, Zurich, Switzerland
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Rob Labruyère
- Swiss Children’s Rehab, University Children’s Hospital Zurich, Mühlebergstrasse 104, 8910 Affoltern am Albis, Switzerland
- Children’s Research Center, University Children’s Hospital of Zurich, University of Zurich, Zurich, Switzerland
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O'Callaghan BPF, Doheny EP, Goulding C, Fortune E, Lowery MM. Adaptive gait segmentation algorithm for walking bout detection using tri-axial accelerometers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:4592-4595. [PMID: 33019016 DOI: 10.1109/embc44109.2020.9176460] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Gait analysis has many potential applications in understanding the activity profiles of individuals in their daily lives, particularly when studying the progression of recovery following injury, or motor deterioration in pathological conditions. One of the many challenges of conducting such analyses in the home environment is the correct and automatic identification of bouts of gait activity. To address this, a novel method for determining bouts of gait from accelerometer data recorded from the shank is presented. This method is fully automated and includes an adaptive thresholding approach which avoids the necessity for identifying subject-specific thresholds. The algorithm was tested on data recorded from 15 healthy subjects during self-selected slow, normal and fast walking speeds ranging from 0.48 ± 0.19 to 1.38 ± 0.33m/s and a single subject with PD walking at their normal walking speed (1.41 ± 0.08m/s) using accelerometers on the shanks. Intra-Class Correlation (ICC) confirmed high levels of agreement between bout onset/offset times and durations estimated using the algorithm, experimentally recorded stopwatch times and manual annotation for the healthy subjects (r=0.975, p <; 0.001; r=0.984, p<; 0.001) and moderate agreement for the PD subject (r=0.663, p<; 0.001). Mean absolute errors between accelerometer-derived and manually-annotated times were calculated, and ranged from 0.91 ± 0.05 s to 1.17 ± 2.26 s for bout onset detection, 0.80 ± 0.23 s to 2.41 ± 3.77 s for offset detection and 1.27 ± 0.13 s to 3.67 ± 4.59 s for bout durations.
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Jeng B, Cederberg KL, Lai B, Sasaki JE, Bamman MM, Motl RW. Step-rate threshold for physical activity intensity in Parkinson's disease. Acta Neurol Scand 2020; 142:145-150. [PMID: 32255504 DOI: 10.1111/ane.13250] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 03/21/2020] [Accepted: 03/31/2020] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To examine the relationship between step-rate and energy expenditure during treadmill walking in persons with PD and then further develop a step-rate cut-point for moderate-to-vigorous physical activity (MVPA) for persons with PD. MATERIALS AND METHODS The sample consisted of 30 persons with mild-to-moderate PD and 30 controls matched by age and sex. Participants performed a 6-minute bout of over-ground walking at comfortable speed, and then completed three, 6-minute bouts of treadmill walking at 13.4 m/min slower, comfortable, and 13.4 m/min faster than comfortable speeds. The three treadmill speeds were based on the initial over-ground walking speed. The total number of steps per treadmill walking bout was recorded using a hand-tally counter, and energy expenditure was measured using a portable, indirect spirometry system. RESULTS The results indicated a strong association between step-rate and energy expenditure for persons with PD (R2 = .92) and controls (R2 = .92). The analyses further indicated a steeper slope of the association for persons with PD compared with controls (t(58) = -1.87, P < .05), resulting in a lower step-rate threshold (t(58) = 2.19, P < .05) for persons with PD (~80 steps·per minutes) than controls (~93 steps·per minutes). CONCLUSION Collectively, these results support the application of this disease-specific step-rate threshold for MVPA among persons with PD. This has important implications for physical activity promotion, prescription, and monitoring using accelerometers and pedometers for persons with PD to manage health and symptoms of PD.
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Affiliation(s)
- Brenda Jeng
- Department of Physical Therapy School of Health Professions University of Alabama at Birmingham Birmingham AL USA
| | - Katie L. Cederberg
- Department of Physical Therapy School of Health Professions University of Alabama at Birmingham Birmingham AL USA
| | - Byron Lai
- Department of Physical Therapy School of Health Professions University of Alabama at Birmingham Birmingham AL USA
| | - Jeffer E. Sasaki
- Graduate Program in Physical Education Federal University of Triângulo Mineiro Uberaba Brazil
| | - Marcas M. Bamman
- UAB Center for Exercise Medicine University of Alabama at Birmingham Birmingham AL USA
- Departments of Cell, Developmental, and Integrative Biology; Medicine; and Neurology University of Alabama at Birmingham Birmingham AL USA
- Geriatric Research, Education, and Clinical Center Birmingham VA Medical Center Birmingham AL USA
| | - Robert W. Motl
- Department of Physical Therapy School of Health Professions University of Alabama at Birmingham Birmingham AL USA
- UAB Center for Exercise Medicine University of Alabama at Birmingham Birmingham AL USA
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Derungs A, Schuster-Amft C, Amft O. Longitudinal Walking Analysis in Hemiparetic Patients Using Wearable Motion Sensors: Is There Convergence Between Body Sides? Front Bioeng Biotechnol 2018; 6:57. [PMID: 29904628 PMCID: PMC5990601 DOI: 10.3389/fbioe.2018.00057] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 04/23/2018] [Indexed: 11/13/2022] Open
Abstract
Background: Longitudinal movement parameter analysis of hemiparetic patients over several months could reveal potential recovery trends and help clinicians adapting therapy strategies to maximize recovery outcome. Wearable sensors offer potential for day-long movement recordings in realistic rehabilitation settings including activities of daily living, e.g., walking. The measurement of walking-related movement parameters of affected and non-affected body sides are of interest to determine mobility and investigate recovery trends. Methods: By comparing movement of both body sides, recovery trends across the rehabilitation duration were investigated. We derived and validated selected walking segments from free-living, day-long movement by using rules that do not require data-based training or data annotations. Automatic stride segmentation using peak detection was applied to walking segments. Movement parameters during walking were extracted, including stride count, stride duration, cadence, and sway. Finally, linear regression models over each movement parameter were derived to forecast the moment of convergence between body sides. Convergence points were expressed as duration and investigated in a patient observation study. Results: Convergence was analyzed in walking-related movement parameters in an outpatient study including totally 102 full-day recordings of inertial movement data from 11 hemiparetic patients. The recordings were performed over several months in a day-care centre. Validation of the walking extraction method from sensor data yielded sensitivities up to 80 % and specificity above 94 % on average. Comparison of automatically and manually derived movement parameters showed average relative errors below 6 % between affected and non-affected body sides. Movement parameter variability within and across patients was observed and confirmed by case reports, reflecting individual patient behavior. Conclusion: Convergence points were proposed as intuitive metric, which could facilitate training personalization for patients according to their individual needs. Our continuous movement parameter extraction and analysis, was feasible for realistic, day-long recordings without annotations. Visualizations of movement parameter trends and convergence points indicated that individual habits and patient therapies were reflected in walking and mobility. Context information of clinical case reports supported trend and convergence interpretation. Inconsistent convergence point estimation suggested individually varying deficiencies. Long-term recovery monitoring using convergence points could support patient-specific training strategies in future remote rehabilitation.
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Affiliation(s)
- Adrian Derungs
- Chair of eHealth and mHealth, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Corina Schuster-Amft
- Research Department, Reha Rheinfelden, Rheinfelden, Switzerland.,Institute for Rehabilitation and Performance Technology, Bern University of Applied Sciences, Burgdorf, Switzerland.,Department of Sport, Exericse and Health, University of Basel, Basel, Switzerland
| | - Oliver Amft
- Chair of eHealth and mHealth, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
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Detecting Steps Walking at very Low Speeds Combining Outlier Detection, Transition Matrices and Autoencoders from Acceleration Patterns. SENSORS 2017; 17:s17102274. [PMID: 28981453 PMCID: PMC5677312 DOI: 10.3390/s17102274] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Revised: 09/28/2017] [Accepted: 10/04/2017] [Indexed: 01/25/2023]
Abstract
In this paper, we develop and validate a new algorithm to detect steps while walking at speeds between 30 and 40 steps per minute based on the data sensed from a single tri-axial accelerometer. The algorithm concatenates three consecutive phases. First, an outlier detection is performed on the sensed data based on the Mahalanobis distance to pre-detect candidate points in the acceleration time series that may contain a ground contact segment of data while walking. Second, the acceleration segment around the pre-detected point is used to calculate the transition matrix in order to capture the time dependencies. Finally, autoencoders, trained with data segments containing ground contact transition matrices from acceleration series from labeled steps are used to reconstruct the computed transition matrices at each pre-detected point. A similarity index is used to assess if the pre-selected point contains a true step in the 30–40 steps per minute speed range. Our experimental results, based on a database from three different participants performing similar activities to the target one, are able to achieve a recall = 0.88 with precision = 0.50 improving the results when directly applying the autoencoders to acceleration patterns (recall = 0.77 with precision = 0.50).
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Godfrey A. Wearables for independent living in older adults: Gait and falls. Maturitas 2017; 100:16-26. [PMID: 28539173 DOI: 10.1016/j.maturitas.2017.03.317] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 03/22/2017] [Indexed: 01/15/2023]
Abstract
Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) is one approach that offers a viable means for ubiquitous, sustainable and scalable monitoring of the health of older adults in habitual free-living environments. Gait has been presented as a relevant (bio)marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research-grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic, with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of the recent literature on gait assessment through the use of wearables, focusing on the need for an algorithm fusion approach to measurement, culminating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Suggestions for how this domain needs to progress are also summarised.
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Affiliation(s)
- A Godfrey
- Newcastle University Business School, Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, United Kingdom; Institute of Neuroscience, Newcastle University Institute for Ageing, Newcastle University, Newcastle upon Tyne, United Kingdom.
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