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Doekes HP, Petie R, de Jong R, Adriaens I, Wisselink HJ, Stockhofe-Zurwieden N. Added Value of Sensor-Based Behavioural Monitoring in an Infectious Disease Study with Sheep Infected with Toxoplasma gondii. Animals (Basel) 2024; 14:1908. [PMID: 38998020 PMCID: PMC11240357 DOI: 10.3390/ani14131908] [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: 05/30/2024] [Revised: 06/21/2024] [Accepted: 06/25/2024] [Indexed: 07/14/2024] Open
Abstract
Sensor technologies are increasingly used to monitor laboratory animal behaviour. The aim of this study was to investigate the added value of using accelerometers and video to monitor the activity and drinking behaviour of three rams from 5 days before to 22 days after inoculation with Toxoplasma gondii. We computed the activity from accelerometer data as the vectorial dynamic body acceleration (VDBA). In addition, we assessed individual drinking behaviour from video, using frame differencing above the drinker to identify drinking bouts, and Aruco markers for individual identification. Four days after inoculation, rams developed fever and activity decreased. The daytime VDBA from days 4 to 10 was 60-80% of that before inoculation. Animal caretakers scored rams as lethargic on days 5 and 6 and, for one ram, also on the morning of day 7. Video analysis showed that each ram decreased its number of visits to the drinker, as well as its time spent at the drinker, by up to 50%. The fever and corresponding sickness behaviours lasted until day 10. Overall, while we recognize the limited conclusiveness due to the small number of animals, the sensor technologies provided continuous, individual, detailed, and objective data and offered additional insights as compared to routine observations. We recommend the wider implementation of such technologies in animal disease trials to refine experiments and guarantee the quality of experimental results.
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Affiliation(s)
- Harmen P. Doekes
- Animal Breeding and Genomics, Department of Animal Sciences, Wageningen University & Research, P.O. Box 338, 6700 AH Wageningen, The Netherlands
- Wageningen Bioveterinary Research, Wageningen University & Research, 8221 RA Lelystad, The Netherlands
| | - Ronald Petie
- Wageningen Bioveterinary Research, Wageningen University & Research, 8221 RA Lelystad, The Netherlands
| | - Rineke de Jong
- Wageningen Bioveterinary Research, Wageningen University & Research, 8221 RA Lelystad, The Netherlands
| | - Ines Adriaens
- Research Group BioVism, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
- Livestock Technology Group, Department of Biosystems, Division of Animal and Human Health Engineering, Kleinhoefstraat 4, 2440 Geel, Belgium
| | - Henk J. Wisselink
- Wageningen Bioveterinary Research, Wageningen University & Research, 8221 RA Lelystad, The Netherlands
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Ramli AA, Liu X, Berndt K, Chuah CN, Goude E, Kaethler LB, Lopez A, Nicorici A, Owens C, Rodriguez D, Wang J, Aranki D, McDonald CM, Henricson EK. Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach. SENSORS (BASEL, SWITZERLAND) 2024; 24:1155. [PMID: 38400313 PMCID: PMC10891633 DOI: 10.3390/s24041155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/03/2024] [Accepted: 01/29/2024] [Indexed: 02/25/2024]
Abstract
Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant's level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body's center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson's r = -0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individual's stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.
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Affiliation(s)
- Albara Ah Ramli
- Department of Computer Science, School of Engineering, University of California, 1 Shields Ave, Davis, CA 95616, USA; (A.A.R.); (X.L.)
| | - Xin Liu
- Department of Computer Science, School of Engineering, University of California, 1 Shields Ave, Davis, CA 95616, USA; (A.A.R.); (X.L.)
| | - Kelly Berndt
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Chen-Nee Chuah
- Department of Electrical and Computer Engineering, School of Engineering, University of California, 1 Shields Ave, Davis, CA 95616, USA
| | - Erica Goude
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Lynea B. Kaethler
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Amanda Lopez
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Alina Nicorici
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Corey Owens
- UC Davis Center for Health and Technology, School of Medicine, University of California Davis, 1 Shields Ave, Davis, CA 95616, USA;
| | - David Rodriguez
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Jane Wang
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Daniel Aranki
- Berkeley School of Information, University of California Berkeley, 1 Shields Ave, Berkeley, CA 94720, USA;
| | - Craig M. McDonald
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
| | - Erik K. Henricson
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of California, 1 Shields Ave, Davis, CA 95616, USA; (K.B.); (E.G.); (L.B.K.); (A.L.); (A.N.); (D.R.); (J.W.); (C.M.M.)
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Real-Time Short-Term Pedestrian Trajectory Prediction Based on Gait Biomechanics. SENSORS 2022; 22:s22155828. [PMID: 35957385 PMCID: PMC9370855 DOI: 10.3390/s22155828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/28/2022] [Accepted: 08/01/2022] [Indexed: 02/01/2023]
Abstract
The short-term prediction of a person’s trajectory during normal walking becomes necessary in many environments shared by humans and robots. Physics-based approaches based on Newton’s laws of motion seem best suited for short-term predictions, but the intrinsic properties of human walking conflict with the foundations of the basic kinematical models compromising their performance. In this paper, we propose a short-time prediction method based on gait biomechanics for real-time applications. This method relays on a single biomechanical variable, and it has a low computational burden, turning it into a feasible solution to implement in low-cost portable devices. We evaluate its performance from an experimental benchmark where several subjects walked steadily over straight and curved paths. With this approach, the results indicate a performance good enough to be applicable to a wide range of human–robot interaction applications.
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Lee S, Song Y, Lee J, Oh J, Lim TH, Ahn C, Kim IY. Development of Smart-Ring-Based Chest Compression Depth Feedback Device for High Quality Chest Compressions: A Proof-of-Concept Study. BIOSENSORS-BASEL 2021; 11:bios11020035. [PMID: 33525710 PMCID: PMC7912179 DOI: 10.3390/bios11020035] [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: 12/14/2020] [Revised: 01/21/2021] [Accepted: 01/25/2021] [Indexed: 12/11/2022]
Abstract
Recently, a smart-device-based chest compression depth (CCD) feedback system that helps ensure that chest compressions have adequate depth during cardiopulmonary resuscitation (CPR) was developed. However, no CCD feedback device has been developed for infants, and many feedback systems are inconvenient to use. In this paper, we report the development of a smart-ring-based CCD feedback device for CPR based on an inertial measurement unit, and propose a high-quality chest compression depth estimation algorithm that considers the orientation of the device. The performance of the proposed feedback system was evaluated by comparing it with a linear variable differential transformer in three CPR situations. The experimental results showed compression depth errors of 2.0 ± 1.1, 2.2 ± 0.9, and 1.4 ± 1.1 mm in the three situations. In addition, we conducted a pilot test with an adult/infant mannequin. The results of the experiments show that the proposed smart-ring-based CCD feedback system is applicable to various chest compression methods based on real CPR situations.
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Affiliation(s)
- Seungjae Lee
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea; (S.L.); (J.L.)
| | - Yeongtak Song
- Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea; (Y.S.); (J.O.); (T.H.L.)
- Convergence Technology Center for Disaster Preparedness, Hanyang University, Seoul 04763, Korea
| | - Jongshill Lee
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea; (S.L.); (J.L.)
| | - Jaehoon Oh
- Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea; (Y.S.); (J.O.); (T.H.L.)
- Convergence Technology Center for Disaster Preparedness, Hanyang University, Seoul 04763, Korea
| | - Tae Ho Lim
- Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea; (Y.S.); (J.O.); (T.H.L.)
- Convergence Technology Center for Disaster Preparedness, Hanyang University, Seoul 04763, Korea
| | - Chiwon Ahn
- Department of Emergency Medicine, College of Medicine, Chung-Ang University, Seoul 06974, Korea;
| | - In Young Kim
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea; (S.L.); (J.L.)
- Correspondence:
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Melendez-Calderon A, Shirota C, Balasubramanian S. Estimating Movement Smoothness From Inertial Measurement Units. Front Bioeng Biotechnol 2021; 8:558771. [PMID: 33520949 PMCID: PMC7841375 DOI: 10.3389/fbioe.2020.558771] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 12/09/2020] [Indexed: 12/11/2022] Open
Abstract
Inertial measurement units (IMUs) are increasingly used to estimate movement quality and quantity to the infer the nature of motor behavior. The current literature contains several attempts to estimate movement smoothness using data from IMUs, many of which assume that the translational and rotational kinematics measured by IMUs can be directly used with the smoothness measures spectral arc length (SPARC) and log dimensionless jerk (LDLJ-V). However, there has been no investigation of the validity of these approaches. In this paper, we systematically evaluate the use of these measures on the kinematics measured by IMUs. We show that: (a) SPARC and LDLJ-V are valid measures of smoothness only when used with velocity; (b) SPARC and LDLJ-V applied on translational velocity reconstructed from IMU is highly error prone due to drift caused by integration of reconstruction errors; (c) SPARC can be applied directly on rotational velocities measured by a gyroscope, but LDLJ-V can be error prone. For discrete translational movements, we propose a modified version of the LDLJ-V measure, which can be applied to acceleration data (LDLJ-A). We evaluate the performance of these measures using simulated and experimental data. We demonstrate that the accuracy of LDLJ-A depends on the time profile of IMU orientation reconstruction error. Finally, we provide recommendations for how to appropriately apply these measures in practice under different scenarios, and highlight various factors to be aware of when performing smoothness analysis using IMU data.
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Affiliation(s)
- Alejandro Melendez-Calderon
- Cereneo Advanced Rehabilitation Institute (CARINg), Vitznau, Switzerland
- Biomedical Engineering Group, School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, QLD, Australia
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Camila Shirota
- The Hopkins Centre, Menzies Health Institute Queensland, Griffith University, Nathan, QLD, Australia
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Department of Neurology, University of Zurich, Zurich, Switzerland
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Poitras I, Clouâtre J, Bouyer LJ, Routhier F, Mercier C, Campeau-Lecours A. Development and Validation of Open-Source Activity Intensity Count and Activity Intensity Classification Algorithms from Raw Acceleration Signals of Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6767. [PMID: 33256127 PMCID: PMC7730347 DOI: 10.3390/s20236767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/17/2020] [Accepted: 11/23/2020] [Indexed: 11/16/2022]
Abstract
BACKGROUND A popular outcome in rehabilitation studies is the activity intensity count, which is typically measured from commercially available accelerometers. However, the algorithms are not openly available, which impairs long-term follow-ups and restricts the potential to adapt the algorithms for pathological populations. The objectives of this research are to design and validate open-source algorithms for activity intensity quantification and classification. METHODS Two versions of a quantification algorithm are proposed (fixed [FB] and modifiable bandwidth [MB]) along with two versions of a classification algorithm (discrete [DM] vs. continuous methods [CM]). The results of these algorithms were compared to those of a commercial activity intensity count solution (ActiLife) with datasets from four activities (n = 24 participants). RESULTS The FB and MB algorithms gave similar results as ActiLife (r > 0.96). The DM algorithm is similar to a ActiLife (r ≥ 0.99). The CM algorithm differs (r ≥ 0.89) but is more precise. CONCLUSION The combination of the FB algorithm with the DM results is a solution close to that of ActiLife. However, the MB version remains valid while being more adaptable, and the CM is more precise. This paper proposes an open-source alternative for rehabilitation that is compatible with several wearable devices and not dependent on manufacturer commercial decisions.
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Affiliation(s)
- Isabelle Poitras
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Quebec City, QC G1M 2S8, Canada; (I.P.); (J.C.); (L.J.B.); (F.R.); (C.M.)
- Department of Rehabilitation, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Jade Clouâtre
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Quebec City, QC G1M 2S8, Canada; (I.P.); (J.C.); (L.J.B.); (F.R.); (C.M.)
- Department of Mechanical Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Laurent J. Bouyer
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Quebec City, QC G1M 2S8, Canada; (I.P.); (J.C.); (L.J.B.); (F.R.); (C.M.)
- Department of Rehabilitation, Laval University, Quebec City, QC G1V 0A6, Canada
| | - François Routhier
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Quebec City, QC G1M 2S8, Canada; (I.P.); (J.C.); (L.J.B.); (F.R.); (C.M.)
- Department of Rehabilitation, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Catherine Mercier
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Quebec City, QC G1M 2S8, Canada; (I.P.); (J.C.); (L.J.B.); (F.R.); (C.M.)
- Department of Rehabilitation, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Alexandre Campeau-Lecours
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Quebec City, QC G1M 2S8, Canada; (I.P.); (J.C.); (L.J.B.); (F.R.); (C.M.)
- Department of Mechanical Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
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Matsumura U, Kai A, Numata M, Lee Y, Yamamoto S, Tsurusaki T. Possible predictive formulas for quantitative and time-based estimation of muscle strength during motion. J Phys Ther Sci 2020; 32:27-32. [PMID: 32082024 PMCID: PMC7008027 DOI: 10.1589/jpts.32.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 10/17/2019] [Indexed: 11/24/2022] Open
Abstract
[Purpose] To examine the validity of the predictive formulas based on the angle
information of the segment center of mass and moments of inertia, and to propose a joint
moment estimation method. [Participants and Methods] Twenty nine young healthy adults were
divided into two groups: the Creation group (20 adults) was needed to create the
prediction formulas, and the Verification group (9 adults) was needed to verify the
formulas. By monitoring the Creation group, the angular information from inertial motion
sensors and moments of inertia of each limb were used to estimate actual ankle joint
moment and knee joint moment. Thereafter, the actual joint moments was derived from the
Verification group and compared to the predicted values via Pearson correlations.
[Results] Good to excellent correlations were obtained between the actual joint moments of
the two groups for most of the motions. [Conclusion] It is suggested that the predictive
formulas created from the angle information of the segment center of mass and moments of
inertia can be used for an approximate estimation of the lower limb joint moments in the
sagittal plane and more clinically useful tools need to be considered in the future.
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Affiliation(s)
- Umi Matsumura
- Health Sciences, Nagasaki University Graduate School of Biomedical Sciences, Japan
| | - Ayana Kai
- Department of Rehabilitation, Kuroki Memorial Hospital, Japan
| | - Miku Numata
- Department of Rehabilitation, Nagasaki North Hospital, Japan
| | - Yeonghee Lee
- Health Sciences, Nagasaki University Graduate School of Biomedical Sciences, Japan
| | - Shimpei Yamamoto
- Health Sciences, Nagasaki University Graduate School of Biomedical Sciences, Japan
| | - Toshiya Tsurusaki
- Institute of Biomedical Sciences, Nagasaki University: 1-7-1 Sakamoto, Nagasaki, Nagasaki 852-8520, Japan
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Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders. SENSORS 2019; 19:s19040840. [PMID: 30781668 PMCID: PMC6412957 DOI: 10.3390/s19040840] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 02/02/2019] [Accepted: 02/15/2019] [Indexed: 11/30/2022]
Abstract
Accurate stride-length estimation is a fundamental component in numerous applications, such as pedestrian dead reckoning, gait analysis, and human activity recognition. The existing stride-length estimation algorithms work relatively well in cases of walking a straight line at normal speed, but their error overgrows in complex scenes. Inaccurate walking-distance estimation leads to huge accumulative positioning errors of pedestrian dead reckoning. This paper proposes TapeLine, an adaptive stride-length estimation algorithm that automatically estimates a pedestrian’s stride-length and walking-distance using the low-cost inertial-sensor embedded in a smartphone. TapeLine consists of a Long Short-Term Memory module and Denoising Autoencoders that aim to sanitize the noise in raw inertial-sensor data. In addition to accelerometer and gyroscope readings during stride interval, extracted higher-level features based on excellent early studies were also fed to proposed network model for stride-length estimation. To train the model and evaluate its performance, we designed a platform to collect inertial-sensor measurements from a smartphone as training data, pedestrian step events, actual stride-length, and cumulative walking-distance from a foot-mounted inertial navigation system module as training labels at the same time. We conducted elaborate experiments to verify the performance of the proposed algorithm and compared it with the state-of-the-art SLE algorithms. The experimental results demonstrated that the proposed algorithm outperformed the existing methods and achieves good estimation accuracy, with a stride-length error rate of 4.63% and a walking-distance error rate of 1.43% using inertial-sensor embedded in smartphone without depending on any additional infrastructure or pre-collected database when a pedestrian is walking in both indoor and outdoor complex environments (stairs, spiral stairs, escalators and elevators) with natural motion patterns (fast walking, normal walking, slow walking, running, jumping).
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