Développement et étude de la validité d'une semelle instrumentée pour le comptage de pas

Les semelles instrumentées sont des dispositifs pouvant être utilisées pour la quantification de pas et la reconnaissance des activités. Il existe plusieurs modèles de semelles instrumentées, avec des niveaux de validité variables. Ce mémoire comprend trois objectifs : 1) faire une revue systématique de la littérature sur la validité de critère des semelles instrumentées existantes pour identifier les postures, les types d’activités et compter les pas, 2) développer une semelle instrumentée et 3) étudier sa validité pour le comptage de pas. Pour l’objectif 1, cinq bases de données ont permis de sélectionner 33 articles sur la validité de critère de seize modèles de semelles instrumentées pour la détection de posture, de type d’activités et de pas. Selon les indicateurs utilisés, les validités de critère varient de 65,8% à 100% pour la reconnaissance des activités et des postures et de 96% à 100% pour la détection de pas. En somme, peu d’études ont utilisé les semelles instrumentées pour le comptage de pas bien qu’elles démontrent une très bonne validité. Pour les objectifs 2 et 3, nous avons équipé une semelle commercialisée de cinq capteurs de pression et testé trois méthodes de traitement des signaux de pression pour la quantification de pas. Ces trois méthodes sont basées sur le signal de chaque capteur de pression, la moyenne ou la somme cumulée des cinq signaux de pression. Les résultats ont montré que notre semelle instrumentée détectait le pas avec un taux de succès de 94,8 ± 9,4% à 99,5 ± 0,4% à des vitesses de marche confortable et de 97,0 ± 6,2% à 99,6 ± 0,4% à des vitesses de marche rapide à l’intérieur et à l’extérieur d’un bâtiment avec les trois méthodes. Toutefois, la méthode basée sur la somme cumulée avait les niveaux de précision plus élevés pour le comptage de pasInstrumented insoles are devices which can be used for quantifying steps and recognizing activities. Validity of many instrumented insoles varies from medium to high. This thesis has three objectives: 1) to systematically review the literature on the validity of existing instrumented insoles for posture, type of activities recognition, and step counting, 2) to develop an instrumented insole and 3) to study its criterion validity for step counting. For objective 1, five databases were used to select 33 articles on criterion validity of sixteen insole models for posture and type of activities recognition, and step detection. According to indicators used, validity values vary from 65.8% to 100% for activities and postures recognition and from 96% to 100% for detection of steps. In summary, few studies have used instrumented insoles for steps counting even though they demonstrated a very good validity. For objectives 2 and 3, we equipped a commercialized insole with five pressure sensors and tested three pressure signal processing methods for step quantification. These three methods are based on signal of each pressure sensor, average or cumulative sum of five pressure signals. Results showed that our instrumented insole detected steps with a success rate varying from 94.8 ± 9.4% to 99.5 ± 0.4% at self-selected walking speeds and from 97.0 ± 6.2% to 99.6 ± 0.4% at maximal walking speeds in indoor and outdoor settings with all three processing methods. However, cumulative sum method had the highest levels of accuracy for step counting

Criterion validity of ActiGraph monitoring devices for step counting and distance measurement in adults and older adults: a systematic review

Background: Wearable activity monitors such as ActiGraph monitoring devices are widely used, especially in research settings. Various research studies have assessed the criterion validity of ActiGraph devices for step counting and distance estimation in adults and older adults. Although several studies have used the ActiGraph devices as a reference system for activity monitoring, there is no summarized evidence of the psychometric properties. The main objective of this systematic review was to summarize evidence related to the criterion validity of ActiGraph monitor‑ ing devices for step counting and distance estimation in adults and/or older adults. Methods: Literature searches were conducted in six databases (Medline (OVID), Embase, IEEExplore, CINAHL, Engi‑ neering Village and Web of Science). Two reviewers independently conducted selection, a quality analysis of articles (using COSMIN and MacDermid’s grids) and data extraction. Results: This review included 21 studies involving 637 participants (age 30.3±7.5 years (for adults) and 82.7±3.3 years (for older adults)). Five ActiGraph devices (7164, GT1M, wGTX+, GT3X+/wGT3X+and wGT3X − BT) were used to collect data at the hip, wrist and ankle to assess various walking and running speeds (ranging from 0.2 m/s to 4.44 m/s) over durations of 2 min to 3 days (13 h 30 mins per day) for step counting and distance esti‑ mation. The ActiGraph GT3X+/wGT3X+and wGT3X − BT had better criterion validity than the ActiGraph 7164, wGTX+and GT1M according to walking and running speeds for step counting. Validity of ActiGraph wGT3X+was good for distance estimation. Conclusion: The ActiGraph wGT3X − BT and GT3X+/wGT3X+have good criterion validity for step counting, under certain conditions related to walking speeds, positioning and data processing

Validity of Instrumented Insoles for Step Counting, Posture and Activity Recognition: A Systematic Review

With the growing interest in daily activity monitoring, several insole designs have been developed to identify postures, detect activities, and count steps. However, the validity of these devices is not clearly established. The aim of this systematic review was to synthesize the available information on the criterion validity of instrumented insoles in detecting postures activities and steps. The literature search through six databases led to 33 articles that met inclusion criteria. These studies evaluated 17 different insole models and involved 290 participants from 16 to 75 years old. Criterion validity was assessed using six statistical indicators. For posture and activity recognition, accuracy varied from 75.0% to 100%, precision from 65.8% to 100%, specificity from 98.1% to 100%, sensitivity from 73.0% to 100%, and identification rate from 66.2% to 100%. For step counting, accuracies were very high (94.8% to 100%). Across studies, different postures and activities were assessed using different criterion validity indicators, leading to heterogeneous results. Instrumented insoles appeared to be highly accurate for steps counting. However, measurement properties were variable for posture and activity recognition. These findings call for a standardized methodology to investigate the measurement properties of such devices

Comparison and validation of pressure and acceleration time-domain waveform models of a smart insole for accurate step count in healthy people

Several studies have shown good accuracies for step count based on pressure signals of smart insoles in people walking at different speeds. Although smart insoles are often equipped with pressure sensors and accelerometer, no study has focused on comparing the accuracy of step count separately based on pressure and acceleration signals in healthy people. The objectives of this study were to design a waveform model of accelerometer and pressure sensors, and then compare with commercially well-known step count devices and validate these models using manual step counter for step count. Eight healthy participants (age: 39.8±17.56 years old) wore a pair of smart insoles, a GaitUp, and a StepWatchTM and performed the six-minute walking test at walking speeds from 1.62 to 2.22 m/s. Four pressure and one acceleration waveform models were designed and used for the detection of 341 to 412 steps. Accuracies ranged from 99.80%±0.60% to 99.97%±1.38% for right side, and from 99.67%±0.63% to 99.90%±0.05% for left side with pressure waveform models. In addition, the acceleration waveform model provided accuracies of 99.87%±2.49% and 99.84%±4.77% for right and left sides respectively. Step count accuracies using the GaitUp were 99.51%±2.06% for right side, and 99.51%±4.32% for left side. Finally, the StepWatchTM yielded step count accuracies of 99.31%±15.95% and 98.52%±28.06% for right and left sides respectively. These results suggested the smart insole with pressure and acceleration waveform models as more accurate than the StepWatchTM and the GaitUp for step count

Design and Accuracy of an Instrumented Insole Using Pressure Sensors for Step Count

Despite the accessibility of several step count measurement systems, count accuracy in real environments remains a major challenge. Microelectromechanical systems and pressure sensors seem to present a potential solution for step count accuracy. The purpose of this study was to equip an insole with pressure sensors and to test a novel and potentially more accurate method of detecting steps. Methods: Five force-sensitive resistors (FSR) were integrated under the heel, the first, third, and fifth metatarsal heads and the great toe. This system was tested with twelve healthy participants at self-selected and maximal walking speeds in indoor and outdoor settings. Step counts were computed based on previously reported calculation methods, individual and averaged FSR-signals, and a new method: cumulative sum of all FSR-signals. These data were compared to a direct visual step count for accuracy analysis. Results: This system accurately detected steps with success rates ranging from 95.5 ± 3.5% to 98.5 ± 2.1% (indoor) and from 96.5 ± 3.9% to 98.0 ± 2.3% (outdoor) for self-selected walking speeds and from 98.1 ± 2.7% to 99.0 ± 0.7% (indoor) and 97.0 ± 6.2% to 99.4 ± 0.7% (outdoor) for maximal walking speeds. Cumulative sum of pressure signals during the stance phase showed high step detection accuracy (99.5 ± 0.7%⁻99.6 ± 0.4%) and appeared to be a valid method of step counting. Conclusions: The accuracy of step counts varied according to the calculation methods, with cumulative sum-based method being highly accurate

Validity of Instrumented Insoles for Step Counting, Posture and Activity Recognition: A Systematic Review

With the growing interest in daily activity monitoring, several insole designs have been developed to identify postures, detect activities, and count steps. However, the validity of these devices is not clearly established. The aim of this systematic review was to synthesize the available information on the criterion validity of instrumented insoles in detecting postures activities and steps. The literature search through six databases led to 33 articles that met inclusion criteria. These studies evaluated 17 different insole models and involved 290 participants from 16 to 75 years old. Criterion validity was assessed using six statistical indicators. For posture and activity recognition, accuracy varied from 75.0% to 100%, precision from 65.8% to 100%, specificity from 98.1% to 100%, sensitivity from 73.0% to 100%, and identification rate from 66.2% to 100%. For step counting, accuracies were very high (94.8% to 100%). Across studies, different postures and activities were assessed using different criterion validity indicators, leading to heterogeneous results. Instrumented insoles appeared to be highly accurate for steps counting. However, measurement properties were variable for posture and activity recognition. These findings call for a standardized methodology to investigate the measurement properties of such devices

Cardiorespiratory strain during stroke rehabilitation: Are patients trained enough? A systematic review

International audienceRehabilitation is a mandatory component of stroke management, aiming to recover functional capacity and independence. To that end, physical therapy sessions must involve adequate intensity in terms of cardiopulmonary stress to meet the physiological demands of independent living