Feasibility of a Wearable, Sensor-based Motion Tracking System

Mohammadzadeh, Farrokh; Liu, Shijing; Bond, K.; Nam, Chang S.

doi:10.1016/j.promfg.2015.07.128

Cited by 8 publications

(7 citation statements)

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“…Motion sensors are usually equipped with three types of sensors: a gyroscope, an accelerometer and a magnetometer. In general, the gyroscope and accelerometer combine data to determine the orientation and location of a monitored body part (Shnayder et al, 2005) and magnetometers are sensors that are used to calibrate the direction of the sensor (Mohammadzadeh et al, 2015).…”

Section: Jeim 314mentioning

confidence: 99%

“…The common feature of the new methods is that the quantity and accuracy of the collected data are high. Radio-frequency identification (RFID), wireless fidelity (WiFi), Bluetooth, infrared (IR), ZigBee (Huang and Chan, 2011;Pan et al, 2014) and computer vision techniques such as camera (Merad et al, 2016;Liu et al, 2015), motion sensor (Mohammadzadeh et al, 2015) and ultrasound are technology alternatives that can be used for collecting indoor location data. RFID technology is a tool that collects and stores data by wireless communicating with tags on the object to be tracked (Seol et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, some results were obtained to control the crowd in the museum (Yoshimura et al, 2014) Zigbee It is a low-cost wireless communication technology based on the principle of minimum power consumption, which can be performed by data exchange in a small size Huang and Chan (2011), Pan et al (2014), Stępień et al (2016) The data collected from the object with Zigbee technology is a work that determines where the object is closest to the k-nearest algorithm (Huang and Chan, 2011) Camera It uses sophisticated image processing algorithms with the complex structure to describe the object to be tracked Merad et al (2016), Wu et al (2015), Liu et al (2015), Chen et al (2014), Esterle et al (2014), Nguyen et al (2017) The roads that customers follow are turned into temperature maps using the in-store camera recordings to understand the behavior of customers and the way they follow (Wu et al, 2015) Motion sensor Motion sensor technology analyzes high-cost, multi-camera images that are sensitive to indoors and target areas Mohammadzadeh et al (2015), Park et al (2017)Bluetooth and motion sensor were used together. A self-correction system was developed for evaluation of the different results obtained from the two methods(Mohammadzadeh et al, 2015) InfraredIt is a data collection technology that can also be used indoors without lightingLiu et al (2017), Yucel et al (2012,Asha and Narasimhadhan (2017),Jovanovic et al (2014)…”

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In-store behavioral analytics technology selection using fuzzy decision making

Doğan

Öztayşi

2018

JEIM

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Purpose With the emerging technologies, collecting and processing data about the behaviors of customers or employees in a specific location has become possible. The purpose of this paper is to evaluate existing data collection technologies. Design/methodology/approach Technology evaluation problem is handled as a multi-criteria decision-making (MCDM) problem. In this manner, a decision model containing four criteria and eight sub-criteria and four alternatives are formed. The problem is solved using hesitant analytic hierarchy process (AHP) and trapezoidal fuzzy numbers (TrFN). Findings The results show that the most important sub-criteria are: accuracy, quantity, ıntrospective and cost. Decision makers’ evaluate for alternatives, namely wireless fidelity (WiFi), camera, radio-frequency identification and Bluetooth. The best alternative is found as Bluetooth which is followed by WiFi and Camera. Research limitations/implications Technology evaluation problem, just like many other MCDM problems are solved using expert evaluations. Thus, the generalizability of the findings is low. Originality/value In this paper, technology selection problem has been handled using hesitant AHP for the first time. In addition, the original methodology is extended by using TrFN to represent the expert evaluations in a better way.

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Section: Jeim 314mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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In-store behavioral analytics technology selection using fuzzy decision making

Doğan

Öztayşi

2018

JEIM

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“…Therefore, multi-sensor fusion could be a promising method to improve EMG-based recognition accuracy for stroke patients. In addition, to optimize the engagement of patients (Witmer and Singer, 1998), serious games have been designed and utilized in rehabilitation systems (Mohammadzadeh et al, 2015), (Heins et al, 2017), (Nissler et al, 2019). The effectiveness of serious games has been shown to show better results than conventional approaches for upper limb motor function rehabilitation (Tăut et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Proposal of a Wearable Multimodal Sensing-Based Serious Games Approach for Hand Movement Training After Stroke

et al. 2022

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Stroke often leads to hand motor dysfunction, and effective rehabilitation requires keeping patients engaged and motivated. Among the existing automated rehabilitation approaches, data glove-based systems are not easy to wear for patients due to spasticity, and single sensor-based approaches generally provided prohibitively limited information. We thus propose a wearable multimodal serious games approach for hand movement training after stroke. A force myography (FMG), electromyography (EMG), and inertial measurement unit (IMU)-based multi-sensor fusion model was proposed for hand movement classification, which was worn on the user’s affected arm. Two movement recognition-based serious games were developed for hand movement and cognition training. Ten stroke patients with mild to moderate motor impairments (Brunnstrom Stage for Hand II-VI) performed experiments while playing interactive serious games requiring 12 activities-of-daily-living (ADLs) hand movements taken from the Fugl Meyer Assessment. Feasibility was evaluated by movement classification accuracy and qualitative patient questionnaires. The offline classification accuracy using combined FMG-EMG-IMU was 81.0% for the 12 movements, which was significantly higher than any single sensing modality; only EMG, only FMG, and only IMU were 69.6, 63.2, and 47.8%, respectively. Patients reported that they were more enthusiastic about hand movement training while playing the serious games as compared to conventional methods and strongly agreed that they subjectively felt that the proposed training could be beneficial for improving upper limb motor function. These results showed that multimodal-sensor fusion improved hand gesture classification accuracy for stroke patients and demonstrated the potential of this proposed approach to be used as upper limb movement training after stroke.

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“…However, most of them focus on homogeneous sensors, and the selection results of feature subsets tend to end at a large scale. Besides, in addition to extracting features directly from the raw data, IMUs can also be used to calculate the rotation of the limbs [30,31]. Similar to the video-based method, this can be used to calculate the kinematic information, joint angles, for example, as a supplement for HAR without increasing the number of sensors and complexity of ARS.…”

Section: Introductionmentioning

confidence: 99%

Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering

Chen

Sun

2021

Sensors

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Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.

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Feasibility of a Wearable, Sensor-based Motion Tracking System

Cited by 8 publications

References 4 publications

In-store behavioral analytics technology selection using fuzzy decision making

In-store behavioral analytics technology selection using fuzzy decision making

Proposal of a Wearable Multimodal Sensing-Based Serious Games Approach for Hand Movement Training After Stroke

Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering

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