A Radar-Based Terrain Mapping Approach for Stair Detection Towards Enhanced Prosthetic Foot Control

Kleiner, Bernhard; Ziegenspeck, Nils; Stolyarov, Roman; Herr, Hugh M.; Schneider, Urs; Verl, Alexander

doi:10.1109/biorob.2018.8487722

Cited by 21 publications

(25 citation statements)

References 9 publications

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“… Krausz et al EES / 5 1 x depth camera Fixed in 1.5m height Stair segmentation strategy from depth ( H , 2015) [ 45 ] 1 x accelerometer with -50° tilt angle sensing information of the environment. Kleiner et al EES / 5 1 x IMU I : thigh Stair detection algorithm through fusion of ( P , 2018) [ 46 ] 1 x radar sensor motion trajectory and radar distance data. Zhang et al EES / 5 1 x IMU I : knee lateral Environmental feature extraction based on ( P , 2019) [ 47 , 48 ] 1 x depth camera neural network depth scene classification.…”

Section: Resultsmentioning

confidence: 99%

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Relying on more sense for enhancing lower limb prostheses control: a review

Tschiedel

Russold

Kaniušas

2020

J NeuroEngineering Rehabil

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Modern lower limb prostheses have the capability to replace missing body parts and improve the patients’ quality of life. However, missing environmental information often makes a seamless adaptation to transitions between different forms of locomotion challenging. The aim of this review is to identify the progress made in this area over the last decade, addressing two main questions: which types of novel sensors for environmental awareness are used in lower limb prostheses, and how do they enhance device control towards more comfort and safety. A literature search was conducted on two Internet databases, PubMed and IEEE Xplore. Based on the criteria for inclusion and exclusion, 32 papers were selected for the review analysis, 18 of those are related to explicit environmental sensing and 14 to implicit environmental sensing. Characteristics were discussed with a focus on update rate and resolution as well as on computing power and energy consumption. Our analysis identified numerous state-of-the-art sensors, some of which are able to “look through” clothing or cosmetic covers. Five control categories were identified, how “next generation prostheses” could be extended. There is a clear tendency towards more upcoming object or terrain prediction concepts using all types of distance and depth-based sensors. Other advanced strategies, such as bilateral gait segmentation from unilateral sensors, could also play an important role in movement-dependent control applications. The studies demonstrated promising accuracy in well-controlled laboratory settings, but it is unclear how the systems will perform in real-world environments, both indoors and outdoors. At the moment the main limitation proves to be the necessity of having an unobstructed field of view.

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Section: Resultsmentioning

confidence: 99%

“…The first and only group using a RADAR sensor for stair detection was Kleiner et al [ 46 ] in 2018. In this application, a radar distance sensor and an IMU were mounted on the thigh of the prosthetic device.…”

Section: Resultsmentioning

confidence: 99%

Relying on more sense for enhancing lower limb prostheses control: a review

Tschiedel

Russold

Kaniušas

2020

J NeuroEngineering Rehabil

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“…One subject was instrumented with a lightweight wearable smartphone camera system (iPhone XS Max); photograph shown in Figure 1A. Unlike limb-mounted systems (Da Silva et al, 2020; Diaz et al, 2018; Hu et al, 2018; Kleiner et al, 2018; Massalin et al, 2018; Rai and Rombokas, 2018; Varol and Massalin, 2016; Zhang et al, 2011; 2019b; 2019c), chest-mounting can provide more stable video recording and allow users to wear pants and long dresses without obstructing the sampled field-of-view. The chest-mount height was approximately 1.3 m from the ground when the participant stood upright.…”

Section: Methodsmentioning

confidence: 99%

“…While most environment recognition systems have been limited to controlled indoor environments and/or prearranged walking circuits (Du et al, 2012; Hu et al, 2018; Khademi and Simon, 2019; Kleiner et al, 2018; Krausz et al, 2015; 2019; Liu et al, 2016; Wang et al, 2013; Zhang et al, 2011; 2019b; 2019c; 2019d), our subject walked around unknown outdoor and indoor real-world environments while collecting images with occlusions, signal noise, and intraclass variations. Data were collected at various times throughout the day to incorporate different lighting conditions.…”

Section: Methodsmentioning

confidence: 99%

“…Preliminary research has shown that supplementing a locomotion mode recognition system with environment information can improve the classification accuracies and decision times compared to excluding terrain information (Huang et al, 2011; Liu et al, 2016; Wang et al, 2013). Several researchers have explored using radar detectors (Kleiner et al, 2018) and laser rangefinders (Liu et al, 2016; Wang et al, 2013; Zhang et al, 2011) for environment sensing. However, wearable vision-based systems can provide more detailed information about the field-of-view and detect physical obstacles in peripheral locations.…”

Section: Introductionmentioning

confidence: 99%

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ExoNet Database: Wearable Camera Images of Human Locomotion Environments

Laschowski

McNally

Wong

et al. 2020

Preprint

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Advances in computer vision and artificial intelligence are allowing researchers to develop environment recognition systems for powered lower-limb exoskeletons and prostheses. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms for classifying human walking environments. To address these limitations, we developed ExoNet - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in scale and diversity, ExoNet contains over 5.6 million RGB images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable camera system throughout the summer, fall, and winter seasons. Approximately 923,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented communal platform to train, develop, and compare next-generation image classification algorithms for human locomotion environment recognition. Besides the control of powered lower-limb exoskeletons and prostheses, applications of ExoNet could extend to humanoids and autonomous legged robots.

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A review of terrain detection systems for applications in locomotion assistance

Dabbagh

Ronsse

2020

Robotics and Autonomous Systems

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A Radar-Based Terrain Mapping Approach for Stair Detection Towards Enhanced Prosthetic Foot Control

Cited by 21 publications

References 9 publications

Relying on more sense for enhancing lower limb prostheses control: a review

Relying on more sense for enhancing lower limb prostheses control: a review

ExoNet Database: Wearable Camera Images of Human Locomotion Environments

A review of terrain detection systems for applications in locomotion assistance

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