Walking Parameters Estimation Based on a Wrist-Mounted Inertial Sensor for a Walker User

Duong, Huu Toan; Suh, Young Soo

doi:10.1109/jsen.2017.2658618

Cited by 16 publications

(4 citation statements)

References 23 publications

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“…pushing a stroller) as shown in [44]. Significant attention has been recently paid by researchers to overcome the challenge of accurately detecting steps in people who require a walking aid [45] through the adoption of wearable inertial sensors, whose consideration for human motion analysis (e.g. on lower-limbs [46–47], upper-limbs [48], or for activity recognition [49]) is nowadays well-established in literature.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of consumer-level and research-grade activity trackers in ambulatory settings in older adults

et al. 2019

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Wrist-worn activity trackers have experienced a tremendous growth lately and studies on the accuracy of mainstream trackers used by older adults are needed. This study explores the performance of six trackers (Fitbit Charge2, Garmin VivoSmart HR+, Philips Health Watch, Withings Pulse Ox, ActiGraph GT9X-BT, Omron HJ-72OITC) for estimating: steps, travelled distance, and heart-rate measurements for a cohort of older adults. Eighteen older adults completed a structured protocol involving walking tasks, simulated household activities, and sedentary activities. Less standardized activities were also included, such as: dusting, using a walking aid, or playing cards, in order to simulate real-life scenarios. Wrist-mounted and chest/waist-mounted devices were used. Gold-standards included treadmill, ECG-based chest strap, direct observation or video recording according to the activity and parameter. Every tracker showed a decreasing accuracy with slower walking speed, which resulted in a significant step under-counting. A large mean absolute percentage error (MAPE) was found for every monitor at slower walking speeds with the lowest reported MAPE at 2 km/h being 7.78%, increasing to 20.88% at 1.5 km/h, and 44.53% at 1 km/h. During household activities, the MAPE climbing up/down-stairs ranged from 8.38–19.3% and 10.06–19.01% (dominant and non-dominant arm), respectively. Waist-worn devices showed a more uniform performance. However, unstructured activities (e.g. dusting, playing cards), and using a walking aid represent a challenge for all wrist-worn trackers as evidenced by large MAPE (> 57.66% for dusting, > 67.32% when using a walking aid). Poor performance in travelled distance estimation was also evident during walking at low speeds and climbing up/down-stairs (MAPE > 71.44% and > 48.3%, respectively). Regarding heart-rate measurement, there was no significant difference (p-values > 0.05) in accuracy between trackers placed on the dominant or non-dominant arm. Concordant with existing literature, while the mean error was limited (between -3.57 bpm and 4.21 bpm), a single heart-rate measurement could be underestimated up to 30 beats-per-minute. This study showed a number of limitations of consumer-level wrist-based activity trackers for older adults. Therefore caution is required when used, in healthcare or in research settings, to measure activity in older adults.

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Section: Discussionmentioning

confidence: 99%

Accuracy of consumer-level and research-grade activity trackers in ambulatory settings in older adults

et al. 2019

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“…Bilateral walking aids such as walkers or rollators may significantly affect the arm movement and thus the acquired accelerometer signals. On the one hand, this can be used to construct specific algorithms for gait assessment when walking aids are used [ 54 ]. This, in turn, may lead to deteriorated performance of algorithms that are not fit for the purpose of walking aid–based gait assessment.…”

Section: Discussionmentioning

confidence: 99%

Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study

Kluge,

Brand,

Micó-Amigo

et al. 2024

JMIR Form Res

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Background Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. Objective The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back–worn inertial sensors. Methods Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back–worn inertial sensors. Results The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. Conclusions Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. Trial Registration ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987 International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2021-050785

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“…Detect push events [16]. Monitoring of gait parameters [13], [20], [21], [43], [49], [58]. Gait asymmetries [5], [6], [13], [14], [44], [55].…”

Section: Autonomoushumanmentioning

confidence: 99%

“…Feedback, if any, is often provided haptically (structure vibration or selective braking) or visually (LEDs) [25], [34], [57]. Other sensors can be used to monitor gait parameter, such as inertial measurement units (IMUs) [20], [45], [49], cameras [10], [15], [48], laser range finder (LRF) [41], [43] or ultrasonic sensors [25], but they are not used to obtain the user intention.…”

Section: B Interfacesmentioning

confidence: 99%

Smart Rollators Aid Devices: Current Trends and Challenges

Verdezoto

Ballesteros

Urdiales

2022

IEEE Trans. Human-Mach. Syst.

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Mobility loss has a major impact on autonomy. Smart rollators have been proposed to enhance human abilities when conventional devices are not enough. Many human-robot interaction systems have been proposed in the last decade in this area. Comparative analysis shows that mechanical issues aside, they mainly differ in i) equipped sensors and actuators; ii) input interface; iii) operation modes, and iv) adaptation capabilities. This paper presents a review and a tentative taxonomy of approaches during the last 6 years. In total, 92 papers have been reviewed. We have discarded works not focused on humanrobot interaction or focused only on mechanical adaptation. A critical analysis is provided after the review and classification, highlighting systems tested with their target population.

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Walking Parameters Estimation Based on a Wrist-Mounted Inertial Sensor for a Walker User

Cited by 16 publications

References 23 publications

Accuracy of consumer-level and research-grade activity trackers in ambulatory settings in older adults

Accuracy of consumer-level and research-grade activity trackers in ambulatory settings in older adults

Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study

Smart Rollators Aid Devices: Current Trends and Challenges

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