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Jacobs D, Farid L, Ferré S, Herraez K, Gracies JM, Hutin E. Evaluation of the Validity and Reliability of Connected Insoles to Measure Gait Parameters in Healthy Adults. SENSORS 2021; 21:s21196543. [PMID: 34640868 PMCID: PMC8512009 DOI: 10.3390/s21196543] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/21/2021] [Accepted: 09/23/2021] [Indexed: 11/16/2022]
Abstract
The continuous, accurate and reliable estimation of gait parameters as a measure of mobility is essential to assess the loss of functional capacity related to the progression of disease. Connected insoles are suitable wearable devices which allow precise, continuous, remote and passive gait assessment. The data of 25 healthy volunteers aged 20 to 77 years were analysed in the study to validate gait parameters (stride length, velocity, stance, swing, step and single support durations and cadence) measured by FeetMe® insoles against the GAITRite® mat reference. The mean values and the values of variability were calculated per subject for GAITRite® and insoles. A t-test and Levene’s test were used to compare the gait parameters for means and variances, respectively, obtained for both devices. Additionally, measures of bias, standard deviation of differences, Pearson’s correlation and intraclass correlation were analysed to explore overall agreement between the two devices. No significant differences in mean and variance between the two devices were detected. Pearson’s correlation coefficients of averaged gait estimates were higher than 0.98 and 0.8, respectively, for unipedal and bipedal gait parameters, supporting a high level of agreement between the two devices. The connected insoles are therefore a device equivalent to GAITRite® to estimate the mean and variability of gait parameters.
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Affiliation(s)
- Damien Jacobs
- FeetMe S.A.S., 157 bd. MacDonald, 75019 Paris, France; (L.F.); (S.F.)
- Correspondence:
| | - Leila Farid
- FeetMe S.A.S., 157 bd. MacDonald, 75019 Paris, France; (L.F.); (S.F.)
| | - Sabine Ferré
- FeetMe S.A.S., 157 bd. MacDonald, 75019 Paris, France; (L.F.); (S.F.)
| | - Kilian Herraez
- UFR de Mathématiques, Université Pierre et Marie Curie, 75005 Paris, France;
| | - Jean-Michel Gracies
- Laboratoire Analyse et Restauration du Mouvement (ARM), Hôpitaux Universitaires Henri Mondor, Assistance Publique des Hôpitaux de Paris (AP-HP), 94000 Créteil, France; (J.-M.G.); (E.H.)
- EA 7377 Bioingénierie, Tissus et Neuroplasticité (BIOTN), Université Paris-Est Créteil (UPEC), 94000 Créteil, France
| | - Emilie Hutin
- Laboratoire Analyse et Restauration du Mouvement (ARM), Hôpitaux Universitaires Henri Mondor, Assistance Publique des Hôpitaux de Paris (AP-HP), 94000 Créteil, France; (J.-M.G.); (E.H.)
- EA 7377 Bioingénierie, Tissus et Neuroplasticité (BIOTN), Université Paris-Est Créteil (UPEC), 94000 Créteil, France
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Hansen C, Beckbauer M, Romijnders R, Warmerdam E, Welzel J, Geritz J, Emmert K, Maetzler W. Reliability of IMU-Derived Static Balance Parameters in Neurological Diseases. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18073644. [PMID: 33807432 PMCID: PMC8037984 DOI: 10.3390/ijerph18073644] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/25/2021] [Accepted: 03/26/2021] [Indexed: 01/03/2023]
Abstract
Static balance is a commonly used health measure in clinical practice. Usually, static balance parameters are assessed via force plates or, more recently, with inertial measurement units (IMUs). Multiple parameters have been developed over the years to compare patient groups and understand changes over time. However, the day-to-day variability of these parameters using IMUs has not yet been tested in a neurogeriatric cohort. The aim of the study was to examine day-to-day variability of static balance parameters of five experimental conditions in a cohort of neurogeriatric patients using data extracted from a lower back-worn IMU. A group of 41 neurogeriatric participants (age: 78 ± 5 years) underwent static balance assessment on two occasions 12-24 h apart. Participants performed a side-by-side stance, a semi-tandem stance, a tandem stance on hard ground with eyes open, and a semi-tandem assessment on a soft surface with eyes open and closed for 30 s each. The intra-class correlation coefficient (two-way random, average of the k raters' measurements, ICC2, k) and minimal detectable change at a 95% confidence level (MDC95%) were calculated for the sway area, velocity, acceleration, jerk, and frequency. Velocity, acceleration, and jerk were calculated in both anterior-posterior (AP) and medio-lateral (ML) directions. Nine to 41 participants could successfully perform the respective balance tasks. Considering all conditions, acceleration-related parameters in the AP and ML directions gave the highest ICC results. The MDC95% values for all parameters ranged from 39% to 220%, with frequency being the most consistent with values of 39-57%, followed by acceleration in the ML (43-55%) and AP direction (54-77%). The present results show moderate to poor ICC and MDC values for IMU-based static balance assessment in neurogeriatric patients. This suggests a limited reliability of these tasks and parameters, which should induce a careful selection of potential clinically relevant parameters.
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Affiliation(s)
- Clint Hansen
- Department of Neurology, University Hospital Schleswig-Holstein, Arnold-Heller-Strasse 3, Haus D, 24105 Kiel, Germany; (M.B.); (R.R.); (E.W.); (J.W.); (J.G.); (K.E.); (W.M.)
- Correspondence:
| | - Maximilian Beckbauer
- Department of Neurology, University Hospital Schleswig-Holstein, Arnold-Heller-Strasse 3, Haus D, 24105 Kiel, Germany; (M.B.); (R.R.); (E.W.); (J.W.); (J.G.); (K.E.); (W.M.)
| | - Robbin Romijnders
- Department of Neurology, University Hospital Schleswig-Holstein, Arnold-Heller-Strasse 3, Haus D, 24105 Kiel, Germany; (M.B.); (R.R.); (E.W.); (J.W.); (J.G.); (K.E.); (W.M.)
- Digital Signal Processing and System Theory, Institute of Electrical and Information Engineering, Kiel University, Kaiserstrasse 2, 24143 Kiel, Germany
| | - Elke Warmerdam
- Department of Neurology, University Hospital Schleswig-Holstein, Arnold-Heller-Strasse 3, Haus D, 24105 Kiel, Germany; (M.B.); (R.R.); (E.W.); (J.W.); (J.G.); (K.E.); (W.M.)
- Digital Signal Processing and System Theory, Institute of Electrical and Information Engineering, Kiel University, Kaiserstrasse 2, 24143 Kiel, Germany
| | - Julius Welzel
- Department of Neurology, University Hospital Schleswig-Holstein, Arnold-Heller-Strasse 3, Haus D, 24105 Kiel, Germany; (M.B.); (R.R.); (E.W.); (J.W.); (J.G.); (K.E.); (W.M.)
| | - Johanna Geritz
- Department of Neurology, University Hospital Schleswig-Holstein, Arnold-Heller-Strasse 3, Haus D, 24105 Kiel, Germany; (M.B.); (R.R.); (E.W.); (J.W.); (J.G.); (K.E.); (W.M.)
| | - Kirsten Emmert
- Department of Neurology, University Hospital Schleswig-Holstein, Arnold-Heller-Strasse 3, Haus D, 24105 Kiel, Germany; (M.B.); (R.R.); (E.W.); (J.W.); (J.G.); (K.E.); (W.M.)
| | - Walter Maetzler
- Department of Neurology, University Hospital Schleswig-Holstein, Arnold-Heller-Strasse 3, Haus D, 24105 Kiel, Germany; (M.B.); (R.R.); (E.W.); (J.W.); (J.G.); (K.E.); (W.M.)
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Yamada Y, Shinkawa K, Kobayashi M, Caggiano V, Nemoto M, Nemoto K, Arai T. Combining Multimodal Behavioral Data of Gait, Speech, and Drawing for Classification of Alzheimer's Disease and Mild Cognitive Impairment. J Alzheimers Dis 2021; 84:315-327. [PMID: 34542076 PMCID: PMC8609704 DOI: 10.3233/jad-210684] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/16/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Gait, speech, and drawing behaviors have been shown to be sensitive to the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, previous studies focused on only analyzing individual behavioral modalities, although these studies suggested that each of these modalities may capture different profiles of cognitive impairments associated with AD. OBJECTIVE We aimed to investigate if combining behavioral data of gait, speech, and drawing can improve classification performance compared with the use of individual modality and if each of these behavioral data can be associated with different cognitive and clinical measures for the diagnosis of AD and MCI. METHODS Behavioral data of gait, speech, and drawing were acquired from 118 AD, MCI, and cognitively normal (CN) participants. RESULTS Combining all three behavioral modalities achieved 93.0% accuracy for classifying AD, MCI, and CN, and only 81.9% when using the best individual behavioral modality. Each of these behavioral modalities was statistically significantly associated with different cognitive and clinical measures for diagnosing AD and MCI. CONCLUSION Our findings indicate that these behaviors provide different and complementary information about cognitive impairments such that classification of AD and MCI is superior to using either in isolation.
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Affiliation(s)
| | | | | | - Vittorio Caggiano
- Healthcare and Life Sciences, IBM Research, Yorktown Heights, NY, USA
| | - Miyuki Nemoto
- Department of Psychiatry, University of Tsukuba Hospital, Tsukuba, Ibaraki, Japan
| | - Kiyotaka Nemoto
- Department of Psychiatry, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Tetsuaki Arai
- Department of Psychiatry, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan
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