Multisensor System for Analyzing the Thigh Movement During Walking

Borghetti, Michela; Serpelloni, Mauro; Sardini, Emilio; Casas, Óscar

doi:10.1109/jsen.2017.2715857

Cited by 15 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, we find that, compared to the thigh, the motion of the shank and foot have higher speed and variable acceleration while walking, which may cause higher accumulated error when calculating the shank or foot angle based on angular velocity detected by IMU. For humans, the thigh segment plays an important role for transmitting force to walk a step, and an IMU sensor mounted on the thigh is always required to detect various human motion intentions (Hornero et al, 2013 ; Lewis and Sahrmann, 2015 ; Borghetti et al, 2017 ). For patients with hemiplegia, monitoring the thigh angle obtained from an IMU sensor can detect abnormal postures and quantify patients' inabilities.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it is necessary to propose a method for gait event detection via one IMU sensor mounted on the thigh. Besides, Borghetti et al shows that ranges of thigh angle have significant differences between different subjects (Borghetti et al, 2017 ). The fixed thresholds for gait event detection cannot meet the requirements of high accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Method for Gait Event Detection of Gait Rehabilitation Robots

Li-shan

et al. 2020

Front. Neurorobot.

View full text Add to dashboard Cite

Accurate gait event detection is necessary for control strategies of gait rehabilitation robots. However, due to personal diversity between individuals, it is a challenge for robots to detect a gait event at various stride frequencies. This paper proposes a novel method for gait event detection of a gait rehabilitation robot using a single inertial sensor mounted on the thigh. A self-adaptive threshold for detecting heel strike is obtained in real time via a linear regression model. Observable thresholds for toe off detection are constant at various stride frequencies. Experiments are conducted based on 20 healthy subjects and six hemiplegic patients wearing a gait rehabilitation robot and walking at various kinds of stride frequencies. The experimental results show that the proposed method can detect heel strike and toe off gait events within an average 2% gait cycle temporal errors and never miss two-gait event detection. Compared to the conventional thresholding method, this work presents a simple and robust application for gait event detection in healthy and hemiplegic subjects by one inertial sensor. The linear regression model can be applicable to different subjects walking at various stride frequencies.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Method for Gait Event Detection of Gait Rehabilitation Robots

Li-shan

et al. 2020

Front. Neurorobot.

View full text Add to dashboard Cite

show abstract

“…The IMUs were calibrated according to the standard calibration procedures (an example is reported in [31]). The relationship between the strain and the resistance change of the stretch sensor is reported in [29] and [32]. The calculated gauge factor was 4.73 and the linear correlation coefficient (R 2 ) was equal to 0.98 in the range 0-10 %.…”

Section: Methodsmentioning

confidence: 99%

Validation of a modular and wearable system for tracking fingers movements

et al. 2020

Self Cite

View full text Add to dashboard Cite

<p class="Abstract">Supervising manual operations performed by workers in industrial environments is crucial in a smart factory. Indeed, the production of products with superior quality, at higher throughput rates and reduced costs with the support of the Industry 4.0 enabling technologies is based on the strict control of all the resources inside the factory, including workers. This paper shows a protocol for validating a new wearable system for tracking finger movements. The wearable system consists of two measuring modules worn on the thumb and on the index measuring flexion and extension of the proximal interphalangeal (PIP) joint by a stretch sensor and rotation of the proximal phalanx (PP) by an inertial measurement unit. An optical system is used to validate the system by capturing specific finger movements. Four movements that simulate typical tasks and gestures, such as grasp and pinch, were performed to validate the proposed system. The maximum root-mean-square error is 3.7 deg for the roll angle of PP. The resistance changes of the stretch sensors with respect to flexion and extension of PIP joint is 0.47 Ohm/deg. The results are useful for the data interpretation when the system is adopted for monitoring finger movements and gestures.</p>

show abstract

“…The stretch sensors (Images SI Inc., New York, NY, USA) are conductive rubber cords of 1.7 mm in diameter and 100 mm long, whose electrical resistance changes when they are stretched [ 25 ]. When relaxed, the sensors have a nominal resistance of 1400 Ω.…”

Section: Smart Brace Designmentioning

confidence: 99%

Smart Brace for Static and Dynamic Knee Laxity Measurement

Bellitti

Borghetti

Lopomo

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Every year in Europe more than 500 thousand injuries that involve the anterior cruciate ligament (ACL) are diagnosed. The ACL is one of the main restraints within the human knee, focused on stabilizing the joint and controlling the relative movement between the tibia and femur under mechanical stress (i.e., laxity). Ligament laxity measurement is clinically valuable for diagnosing ACL injury and comparing possible outcomes of surgical procedures. In general, knee laxity assessment is manually performed and provides information to clinicians which is mainly subjective. Only recently quantitative assessment of knee laxity through instrumental approaches has been introduced and become a fundamental asset in clinical practice. However, the current solutions provide only partial information about either static or dynamic laxity. To support a multiparametric approach using a single device, an innovative smart knee brace for knee laxity evaluation was developed. Equipped with stretchable strain sensors and inertial measurement units (IMUs), the wearable system was designed to provide quantitative information concerning the drawer, Lachman, and pivot shift tests. We specifically characterized IMUs by using a reference sensor. Applying the Bland–Altman method, the limit of agreement was found to be less than 0.06 m/s2 for the accelerometer, 0.06 rad/s for the gyroscope and 0.08 μT for the magnetometer. By using an appropriate characterizing setup, the average gauge factor of the three strain sensors was 2.169. Finally, we realized a pilot study to compare the outcomes with a marker-based optoelectronic stereophotogrammetric system to verify the validity of the designed system. The preliminary findings for the capability of the system to discriminate possible ACL lesions are encouraging; in fact, the smart brace could be an effective support for an objective and quantitative diagnosis of ACL tear by supporting the simultaneous assessment of both rotational and translational laxity. To obtain reliable information about the real effectiveness of the system, further clinical validation is necessary.

show abstract

Multisensor System for Analyzing the Thigh Movement During Walking

Cited by 15 publications

References 26 publications

An Adaptive Method for Gait Event Detection of Gait Rehabilitation Robots

An Adaptive Method for Gait Event Detection of Gait Rehabilitation Robots

Validation of a modular and wearable system for tracking fingers movements

Smart Brace for Static and Dynamic Knee Laxity Measurement

Contact Info

Product

Resources

About