New generation of wearable goniometers for motion capture systems

Tognetti, Alessandro; Lorussi, Federico; Mura, Gabriele Dalle; Carbonaro, Nicola; Pacelli, M.; Paradiso, R.; Rossi, Danilo De

doi:10.1186/1743-0003-11-56

Cited by 76 publications

(83 citation statements)

References 27 publications

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“…(6,7) Their electronic goniometer with a double-layered configuration shows promising performance even for stretching properties. However, when a stretch sensor is applied to the human body, the elongation capability of the sensor against the actual stretching of the skin is an important and critical factor.…”

Section: Introductionmentioning

confidence: 99%

Human Joint Angle Estimation with Multiple-strip E-textile Stretching Sensor

Han¹,

Ryu²,

Kim³

2018

Sensors and Materials

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There have been many studies on strain-resistance or piezoresistance e-textile sensors. However, the important and critical factor, i.e., the elongation capability of the sensor against the actual stretching of the skin, has not been addressed yet. In this study, we propose a human joint angle estimation method with e-textile strain-resistance sensors. The stretching of the skin, on which the sensor is attached, is visually analyzed in terms of the stretching capability of the sensor and the actual stretching length of the skin. The overall system platform used to obtain and process the sensor data is described. The proposed system is implemented and applied to the elbow joint of a healthy subject. It was found that the sensors should be attached to the skin properly, especially when the skin is not stretched. It is necessary to consider the stretching capability of the sensor to accommodate the actual stretching of the skin. The two sensors attached on the lateral and medial sides provide complementary information, which can be utilized in a sensor fusion context.

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Section: Introductionmentioning

confidence: 99%

Human Joint Angle Estimation with Multiple-strip E-textile Stretching Sensor

Han¹,

Ryu²,

Kim³

2018

Sensors and Materials

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“…The results obtained have shown the possibility to use personal wearable devices to assist and support chronic patients [1][2][3][4][5][6][7], elderly people [8,9], emergency operators [10,11] and also healthy subjects for sports, wellness and prevention [12][13][14]. At the same time, the wearable technology market has exploded and is expected to further increase over the next few years, as proved by the growing interest of big players such as Google, Apple and Samsung.…”

Section: Introductionmentioning

confidence: 99%

Assessment of a Smart Sensing Shoe for Gait Phase Detection in Level Walking

2016

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Gait analysis and more specifically ambulatory monitoring of temporal and spatial gait parameters may open relevant fields of applications in activity tracking, sports and also in the assessment and treatment of specific diseases. Wearable technology can boost this scenario by spreading the adoption of monitoring systems to a wide set of healthy users or patients. In this context, we assessed a recently developed commercial smart shoe-the FootMoov-for automatic gait phase detection in level walking. FootMoov has built-in force sensors and a triaxial accelerometer and is able to transmit the sensor data to the smartphone through a wireless connection. We developed a dedicated gait phase detection algorithm relying both on force and inertial information. We tested the smart shoe on ten healthy subjects in free level walking conditions and in a laboratory setting in comparison with an optical motion capture system. Results confirmed a reliable detection of the gait phases. The maximum error committed, on the order of 44.7 ms, is comparable with previous studies. Our results confirmed the possibility to exploit consumer wearable devices to extract relevant parameters to improve the subject health or to better manage his/her progressions.

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“…The sensor output is the electrical resistance difference (∆R) of the two sensing layers. We demonstrated earlier that the sensor output is proportional to the flexion angle (θ) [14], which is the angle delimited by the tangent planes to the sensor extremities (Figure 3b).…”

Section: Sensing Glovementioning

confidence: 99%

“…Figure 2a shows the position of the goniometers on the glove, while Figure 2b show the final prototype of the glove where the goniometers are insulated with an additional layer of black fabric. The textile goniometers are double layer angular sensors, as previously described in [14,15]. The sensing layers are knitted piezoresistive fabrics (KPF) that are made of 75% electro-conductive yarn and 25% Lycra [16,17].…”

Section: Sensing Glovementioning

confidence: 99%

Dynamic Gesture Recognition Using a Smart Glove in Hand-Assisted Laparoscopic Surgery

et al. 2018

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This paper presents a methodology for movement recognition in hand-assisted laparoscopic surgery using a textile-based sensing glove. The aim is to recognize the commands given by the surgeon's hand inside the patient's abdominal cavity in order to guide a collaborative robot. The glove, which incorporates piezoresistive sensors, continuously captures the degree of flexion of the surgeon's fingers. These data are analyzed throughout the surgical operation using an algorithm that detects and recognizes some defined movements as commands for the collaborative robot. However, hand movement recognition is not an easy task, because of the high variability in the motion patterns of different people and situations. The data detected by the sensing glove are analyzed using the following methodology. First, the patterns of the different selected movements are defined. Then, the parameters of the movements for each person are extracted. The parameters concerning bending speed and execution time of the movements are modeled in a prephase, in which all of the necessary information is extracted for subsequent detection during the execution of the motion. The results obtained with 10 different volunteers show a high degree of precision and recall.

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New generation of wearable goniometers for motion capture systems

Cited by 76 publications

References 27 publications

Human Joint Angle Estimation with Multiple-strip E-textile Stretching Sensor

Human Joint Angle Estimation with Multiple-strip E-textile Stretching Sensor

Assessment of a Smart Sensing Shoe for Gait Phase Detection in Level Walking

Dynamic Gesture Recognition Using a Smart Glove in Hand-Assisted Laparoscopic Surgery

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