Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7936031
V. Rajendra, O. Dehzangi
Distracted driving is the major cause for injuries and fatalities due to road accidents. Driving is a continuous task which requires constant attention of the driver; a certain level of distraction can cause the driver lose his/her attention to the driving task which might lead to an accident. Thus, detection of distraction will help reduce the number of accidents. There has been much research conducted for automatic detection of driver distraction. Many previous approaches have employed camera based techniques. However these methods might detect the distraction rather late to warn the drivers. On the other hand, neurophysiological signals using Electroencephalography (EEG) have shown to be reliable indicator of distraction. However EEG signals are very complex and the technology is intrusive to the drivers, which creates serious doubt for its practical applications. The objective of this study is to investigate if Galvanic Skin Responses (GSR) can be used to detect distraction under naturalistic driving condition using a wrist band wearable. Six driver subjects participated in our realistic driving experiments. Our experimental results demonstrated high accuracies of detection under subject dependents scenarios. We also investigated the possibility of subject independent distraction detection employing non-linear space transformation based on kernel analysis and support vector machines (SVM).
{"title":"Detection of distraction under naturalistic driving using Galvanic Skin Responses","authors":"V. Rajendra, O. Dehzangi","doi":"10.1109/BSN.2017.7936031","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936031","url":null,"abstract":"Distracted driving is the major cause for injuries and fatalities due to road accidents. Driving is a continuous task which requires constant attention of the driver; a certain level of distraction can cause the driver lose his/her attention to the driving task which might lead to an accident. Thus, detection of distraction will help reduce the number of accidents. There has been much research conducted for automatic detection of driver distraction. Many previous approaches have employed camera based techniques. However these methods might detect the distraction rather late to warn the drivers. On the other hand, neurophysiological signals using Electroencephalography (EEG) have shown to be reliable indicator of distraction. However EEG signals are very complex and the technology is intrusive to the drivers, which creates serious doubt for its practical applications. The objective of this study is to investigate if Galvanic Skin Responses (GSR) can be used to detect distraction under naturalistic driving condition using a wrist band wearable. Six driver subjects participated in our realistic driving experiments. Our experimental results demonstrated high accuracies of detection under subject dependents scenarios. We also investigated the possibility of subject independent distraction detection employing non-linear space transformation based on kernel analysis and support vector machines (SVM).","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122851260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7935995
Shantanu V. Deshmukh, O. Dehzangi
Many of the fatalities involved on-road accidents are associated with driver distraction. In order to reduce the possible chances of road disasters, it is essential to characterize the pre-requisites of driver distraction. While driving, the driver might get distracted by several ways such as talking on the cell phone, texting, and having a conversation with the passenger. There has been extensive research conducted to estimate driver states in recent years particularly on camera and EEG-based systems. However, camera-based systems face challenges such as privacy or latency in detection. On the other hand, Electroencephalography (EEG) based detection can accomplish more reliable detection. However, this technology requires an intrusive implementation. Electrocardiogram (ECG) is a reliable signal which can characterize the physiological changes consistently, with minimal intrusiveness, and at low cost. In this paper, we propose an ECG signal processing recipe with the aim of predicting driver distraction in real-time. Six drivers actively participated in the naturalistic driving experiment where distraction was induced by: 1) making a phone call and 2) having an active conversation between the driver and the passenger. We present an effective frequency subBand analysis using Wavelet Packet Transform (WPT). Due to high dimensionality of the original WPT features, we then applied Principle Component Analysis (PCA) for feature space dimensionality reduction. Based on our experimental results, WPT features demonstrated high information content and provided a significant statistical difference between normal vs. distracted driving scenarios.
{"title":"Identification of real-time driver distraction using optimal subBand detection powered by Wavelet Packet Transform","authors":"Shantanu V. Deshmukh, O. Dehzangi","doi":"10.1109/BSN.2017.7935995","DOIUrl":"https://doi.org/10.1109/BSN.2017.7935995","url":null,"abstract":"Many of the fatalities involved on-road accidents are associated with driver distraction. In order to reduce the possible chances of road disasters, it is essential to characterize the pre-requisites of driver distraction. While driving, the driver might get distracted by several ways such as talking on the cell phone, texting, and having a conversation with the passenger. There has been extensive research conducted to estimate driver states in recent years particularly on camera and EEG-based systems. However, camera-based systems face challenges such as privacy or latency in detection. On the other hand, Electroencephalography (EEG) based detection can accomplish more reliable detection. However, this technology requires an intrusive implementation. Electrocardiogram (ECG) is a reliable signal which can characterize the physiological changes consistently, with minimal intrusiveness, and at low cost. In this paper, we propose an ECG signal processing recipe with the aim of predicting driver distraction in real-time. Six drivers actively participated in the naturalistic driving experiment where distraction was induced by: 1) making a phone call and 2) having an active conversation between the driver and the passenger. We present an effective frequency subBand analysis using Wavelet Packet Transform (WPT). Due to high dimensionality of the original WPT features, we then applied Principle Component Analysis (PCA) for feature space dimensionality reduction. Based on our experimental results, WPT features demonstrated high information content and provided a significant statistical difference between normal vs. distracted driving scenarios.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121897618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7936021
M. Berthelot, Guang-Zhong Yang, Benny P. L. Lo
Blood flow, posture and phenotype (such as age, sex, smoking habit or physical activity) are closely related to vascular health. Episodic monitoring of the vascular system in clinical setting can lead to late diagnose. Inexpensive wearable devices for continuous monitoring of vascular parameters have been widely used, however, they often have limitations in data interpretation: changes in the environment setting can significantly affect the meaning of the results. This paper proposes a low cost networked body worn sensors for real-time analysis of hemodynamics and reports preliminary results on the relation between blood flow (measured through pulse arrival time (PAT)), the effect of postures and age ranges based on experiments with 13 volunteers of different age ranges (<25 years old and >50 years old). Standing, supine and sitting postures were investigated while photoplethysmograph (PPG) sensors were placed at different locations (ear, wrist and ankle). Results show the PAT changes according to the investigated locations and postures for both age group. Also, the average PAT values of the older group are generally higher than those of the younger group. In the older group, the average PAT value is higher for the supine posture than that of the sitting posture which is itself higher than that of the standing posture. In the younger group, the average PAT is higher in supine than that of the sitting and standing postures which have similar average PAT values. This indicates that hemodynamics vary with posture and age.
{"title":"Preliminary study for hemodynamics monitoring using a wearable device network","authors":"M. Berthelot, Guang-Zhong Yang, Benny P. L. Lo","doi":"10.1109/BSN.2017.7936021","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936021","url":null,"abstract":"Blood flow, posture and phenotype (such as age, sex, smoking habit or physical activity) are closely related to vascular health. Episodic monitoring of the vascular system in clinical setting can lead to late diagnose. Inexpensive wearable devices for continuous monitoring of vascular parameters have been widely used, however, they often have limitations in data interpretation: changes in the environment setting can significantly affect the meaning of the results. This paper proposes a low cost networked body worn sensors for real-time analysis of hemodynamics and reports preliminary results on the relation between blood flow (measured through pulse arrival time (PAT)), the effect of postures and age ranges based on experiments with 13 volunteers of different age ranges (<25 years old and >50 years old). Standing, supine and sitting postures were investigated while photoplethysmograph (PPG) sensors were placed at different locations (ear, wrist and ankle). Results show the PAT changes according to the investigated locations and postures for both age group. Also, the average PAT values of the older group are generally higher than those of the younger group. In the older group, the average PAT value is higher for the supine posture than that of the sitting posture which is itself higher than that of the standing posture. In the younger group, the average PAT is higher in supine than that of the sitting and standing postures which have similar average PAT values. This indicates that hemodynamics vary with posture and age.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128380234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7936002
Mert Sevil, Iman Hajizadeh, S. Samadi, Jianyuan Feng, Caterina Lazaro Martinez, Nicole Frantz, Xia Yu, Rachel Brandt, Zacharie Maloney, A. Çinar
Stress causes many physiological changes in the body and has significant effects on physiology. Various types of acute stress include social, competition, emotional and mental stress. Several studies and experiments have been conducted to investigate stress detection and measurement with physiological signals. We designed social and competition stress experiments to test our algorithms to discriminate between stress and non-stress states with physiological signals from an Empatica wristband. The algorithms were successful in detecting the presence of stress with approximately 87% accuracy.
{"title":"Social and competition stress detection with wristband physiological signals","authors":"Mert Sevil, Iman Hajizadeh, S. Samadi, Jianyuan Feng, Caterina Lazaro Martinez, Nicole Frantz, Xia Yu, Rachel Brandt, Zacharie Maloney, A. Çinar","doi":"10.1109/BSN.2017.7936002","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936002","url":null,"abstract":"Stress causes many physiological changes in the body and has significant effects on physiology. Various types of acute stress include social, competition, emotional and mental stress. Several studies and experiments have been conducted to investigate stress detection and measurement with physiological signals. We designed social and competition stress experiments to test our algorithms to discriminate between stress and non-stress states with physiological signals from an Empatica wristband. The algorithms were successful in detecting the presence of stress with approximately 87% accuracy.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"76 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131544232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7936003
John F. Morales, C. Varon, Margot Deviaene, Pascal Borzée, D. Testelmans, B. Buyse, S. Huffel
Sleep Apnea Hypopnea Syndrome (SAHS) is a sleep disorder where patients experience multiple airflow cessations or reductions during the night. It is recognized as a common condition with a population prevalence of 1% to 6.5%. The Apnea Hypopnea Index (AHI) characterizes the severity of SAHS using signals obtained from Polysomnography (PSG); this requires the use of multiple sensors on the patient and an overnight hospital stay. The development of cheaper and more comfortable screening techniques involving wearable devices is, therefore, desirable. This paper presents a method based on wavelet decomposition and phase space reconstruction with embedding dimensions for feature extraction from oxygen saturation measured in SpO2 signals. The proposed characteristics are the areas spanned by each wavelet level in the phase space calculated using the convex hull algorithm. These areas are then fed into a classifier that groups the patients in categories of AHI higher or lower than 5. The results show an accuracy of 93% using K-Nearest Neighbors (Knn), and 88.61% using Least Square Support Vector Machines (LS-SVM).
{"title":"Sleep Apnea Hypopnea Syndrome classification in SpO2 signals using wavelet decomposition and phase space reconstruction","authors":"John F. Morales, C. Varon, Margot Deviaene, Pascal Borzée, D. Testelmans, B. Buyse, S. Huffel","doi":"10.1109/BSN.2017.7936003","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936003","url":null,"abstract":"Sleep Apnea Hypopnea Syndrome (SAHS) is a sleep disorder where patients experience multiple airflow cessations or reductions during the night. It is recognized as a common condition with a population prevalence of 1% to 6.5%. The Apnea Hypopnea Index (AHI) characterizes the severity of SAHS using signals obtained from Polysomnography (PSG); this requires the use of multiple sensors on the patient and an overnight hospital stay. The development of cheaper and more comfortable screening techniques involving wearable devices is, therefore, desirable. This paper presents a method based on wavelet decomposition and phase space reconstruction with embedding dimensions for feature extraction from oxygen saturation measured in SpO2 signals. The proposed characteristics are the areas spanned by each wavelet level in the phase space calculated using the convex hull algorithm. These areas are then fed into a classifier that groups the patients in categories of AHI higher or lower than 5. The results show an accuracy of 93% using K-Nearest Neighbors (Knn), and 88.61% using Least Square Support Vector Machines (LS-SVM).","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131105267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7936041
Daniel R. Freer, Jindong Liu, Guang-Zhong Yang
To functionally aid patients suffering from neurological disorder, a 3 degrees-of-freedom (DoF) upper limb wearable robot is presented (Fig. 1). In order to provide seamless user assistance, the intention of the wearer must be determined. As a sensing mechanism, electromyographic (EMG) signals have commonly been used to estimate human movement. In this study, the effectiveness of movement recognition using a generalized 8-port EMG sensor (Myo Armband) around the forearm was evaluated. Four fundamental movements of the arm (wrist flexion/extension and forearm pronation/supination) were classified using a neural network (NN) with a single hidden layer. The classification method was optimized through analysis of pre-processing algorithms and window size (0.25 to 1 second) to reduce computational expense and maintain classification accuracy. Through these accomplishments, significant groundwork has been provided for the development of a robust and non-invasive solution to tremor of the upper limb.
{"title":"Optimization of EMG movement recognition for use in an upper limb wearable robot","authors":"Daniel R. Freer, Jindong Liu, Guang-Zhong Yang","doi":"10.1109/BSN.2017.7936041","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936041","url":null,"abstract":"To functionally aid patients suffering from neurological disorder, a 3 degrees-of-freedom (DoF) upper limb wearable robot is presented (Fig. 1). In order to provide seamless user assistance, the intention of the wearer must be determined. As a sensing mechanism, electromyographic (EMG) signals have commonly been used to estimate human movement. In this study, the effectiveness of movement recognition using a generalized 8-port EMG sensor (Myo Armband) around the forearm was evaluated. Four fundamental movements of the arm (wrist flexion/extension and forearm pronation/supination) were classified using a neural network (NN) with a single hidden layer. The classification method was optimized through analysis of pre-processing algorithms and window size (0.25 to 1 second) to reduce computational expense and maintain classification accuracy. Through these accomplishments, significant groundwork has been provided for the development of a robust and non-invasive solution to tremor of the upper limb.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122663535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7936007
Jessica E. T. Kabigting, A. Chen, E. J. Chang, Wei-Ning Lee, R. Roberts
Advances in wireless microelectronics and low-cost sensor manufacturing have led to a variety of wearable technologies, with many wearable devices today being used for monitoring health and wellness. Traditional Chinese Medicine (TCM) is a relatively unexplored area of interest as a type of ‘alternative medicine’. In this paper, we evaluated the suitability of a prototype system based on a 3-sensor array of 3 MEMS barometers for TCM pulse-taking applications: this included characterization of the sensitivity, thermal, and temporal response and its effectiveness in measuring pressure waveforms in a physiologic simulation with a graded-pressure fluid flowing through in an artery-mimicking phantom. Our results demonstrated that the prototype was adequate for such applications and confirmed the optimal specifications for the sensor casting rubber (5.7 mm thick) and design.
{"title":"MEMS pressure sensor array wearable for Traditional Chinese Medicine pulse-taking","authors":"Jessica E. T. Kabigting, A. Chen, E. J. Chang, Wei-Ning Lee, R. Roberts","doi":"10.1109/BSN.2017.7936007","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936007","url":null,"abstract":"Advances in wireless microelectronics and low-cost sensor manufacturing have led to a variety of wearable technologies, with many wearable devices today being used for monitoring health and wellness. Traditional Chinese Medicine (TCM) is a relatively unexplored area of interest as a type of ‘alternative medicine’. In this paper, we evaluated the suitability of a prototype system based on a 3-sensor array of 3 MEMS barometers for TCM pulse-taking applications: this included characterization of the sensitivity, thermal, and temporal response and its effectiveness in measuring pressure waveforms in a physiologic simulation with a graded-pressure fluid flowing through in an artery-mimicking phantom. Our results demonstrated that the prototype was adequate for such applications and confirmed the optimal specifications for the sensor casting rubber (5.7 mm thick) and design.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"86 23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126289929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-09DOI: 10.1109/BSN.2017.7935711
X. Long, P. Fonseca, R. Haakma, Ronald M. Aarts
This paper presents an actigraphy-based approach for sleep/wake detection for insomniacs. Due to its relative unobtrusiveness, actigraphy is often used to estimate overnight sleep-wake patterns in clinical practice. However, its performance has been shown to be limited in subjects with sleep complaints such as insomniacs. Quantifying activity counts on 30-s epoch basis, as usually done in regular actigraphy, may lead to an underestimation of wake periods where the subject shows reduced body movements. We therefore propose a new actigraphic feature to characterize the ‘possibility’ of epochs being asleep (or awake) before or after its nearest epoch with a very high activity levels. It is expected to correctly identify some wake epochs when they are very close to the high activity epochs, although they can be motionless. A data set containing 25 insomnia subjects and a linear discriminant classifier were used to test our approach in this study. Leave-one-subject-out cross validation results show that combining the new and the traditional actigraphic features led to a markedly improved performance in sleep/wake detection compared to that using the traditional feature only, with an increase in Cohen's kappa from 0.49 to 0.55.
{"title":"Actigraphy-based sleep/wake detection for insomniacs","authors":"X. Long, P. Fonseca, R. Haakma, Ronald M. Aarts","doi":"10.1109/BSN.2017.7935711","DOIUrl":"https://doi.org/10.1109/BSN.2017.7935711","url":null,"abstract":"This paper presents an actigraphy-based approach for sleep/wake detection for insomniacs. Due to its relative unobtrusiveness, actigraphy is often used to estimate overnight sleep-wake patterns in clinical practice. However, its performance has been shown to be limited in subjects with sleep complaints such as insomniacs. Quantifying activity counts on 30-s epoch basis, as usually done in regular actigraphy, may lead to an underestimation of wake periods where the subject shows reduced body movements. We therefore propose a new actigraphic feature to characterize the ‘possibility’ of epochs being asleep (or awake) before or after its nearest epoch with a very high activity levels. It is expected to correctly identify some wake epochs when they are very close to the high activity epochs, although they can be motionless. A data set containing 25 insomnia subjects and a linear discriminant classifier were used to test our approach in this study. Leave-one-subject-out cross validation results show that combining the new and the traditional actigraphic features led to a markedly improved performance in sleep/wake detection compared to that using the traditional feature only, with an increase in Cohen's kappa from 0.49 to 0.55.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129138591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-01DOI: 10.1109/BSN.2017.7936000
Jonathan C. Maglott, Junkai Xu, P. Shull
Arm motion timing is critical during basketball shooting. This study used a body-worn, sensorized basketball sleeve to identify arm motion timing characteristics during basketball free throw and jump shot shooting for trained and novice shooters. Current basketball shooting research has typically focused on arm kinematic angles, while shot timing has received comparatively less attention. An experiment was conducted to compare arm motion timing between trained and novice shooters while shooting free throws, and a second experiment compared arm motion timing between free throws and jump shots by trained shooters. Trained shooters shot free throws significantly faster than novice shooters, and trained shooters shot jump shots significantly faster than free throws at the same distance from the basket. Knowledge of arm motion timing characteristics from this study could enable future training for improved shooter accuracy.
{"title":"Differences in arm motion timing characteristics for basketball free throw and jump shooting via a body-worn sensorized sleeve","authors":"Jonathan C. Maglott, Junkai Xu, P. Shull","doi":"10.1109/BSN.2017.7936000","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936000","url":null,"abstract":"Arm motion timing is critical during basketball shooting. This study used a body-worn, sensorized basketball sleeve to identify arm motion timing characteristics during basketball free throw and jump shot shooting for trained and novice shooters. Current basketball shooting research has typically focused on arm kinematic angles, while shot timing has received comparatively less attention. An experiment was conducted to compare arm motion timing between trained and novice shooters while shooting free throws, and a second experiment compared arm motion timing between free throws and jump shots by trained shooters. Trained shooters shot free throws significantly faster than novice shooters, and trained shooters shot jump shots significantly faster than free throws at the same distance from the basket. Knowledge of arm motion timing characteristics from this study could enable future training for improved shooter accuracy.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115611651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-05-01DOI: 10.1109/BSN.2017.7936025
Asma Qureshi, M. Engelhard, Maite Brandt-Pearce, M. Goldman
Multiple sclerosis (MS) interrupts communication between the brain and other parts of the body causing functional deterioration. Gait impairment is a common finding in MS, one caused by several neurological symptoms. We perform an event-specific analysis to study the variable impact of MS on gait components. Our results show that the mid-swing to heel strike (HS) phase of a gait cycle is the most indicative of motor problems. We apply the Hilbert-Huang transform to inertial gait data, corresponding to this phase, to extract the spectral features and study their relationships with the patient-reported outcomes. A number of strong and statistically significant dependencies were found, many having to do with activities of daily living and MS walking scale, leading to the conclusion that the disturbance in mid-swing to HS is specific to deterioration in physical functions. Spearman correlations coefficients and adjusted R2 obtained using stepwise linear regression models are reported. We conclude that event-specific gait features can be used to quantify the precise impact of MS symptoms on gait phases and identify markers of balance, stability, or fall risk, etc. We believe that this information supplements on-going MS research and could be used to develop personalized disease-modifying therapies and exercises.
{"title":"Demonstrating the real-world significance of the mid-swing to heel strike part of the gait cycle using spectral features","authors":"Asma Qureshi, M. Engelhard, Maite Brandt-Pearce, M. Goldman","doi":"10.1109/BSN.2017.7936025","DOIUrl":"https://doi.org/10.1109/BSN.2017.7936025","url":null,"abstract":"Multiple sclerosis (MS) interrupts communication between the brain and other parts of the body causing functional deterioration. Gait impairment is a common finding in MS, one caused by several neurological symptoms. We perform an event-specific analysis to study the variable impact of MS on gait components. Our results show that the mid-swing to heel strike (HS) phase of a gait cycle is the most indicative of motor problems. We apply the Hilbert-Huang transform to inertial gait data, corresponding to this phase, to extract the spectral features and study their relationships with the patient-reported outcomes. A number of strong and statistically significant dependencies were found, many having to do with activities of daily living and MS walking scale, leading to the conclusion that the disturbance in mid-swing to HS is specific to deterioration in physical functions. Spearman correlations coefficients and adjusted R2 obtained using stepwise linear regression models are reported. We conclude that event-specific gait features can be used to quantify the precise impact of MS symptoms on gait phases and identify markers of balance, stability, or fall risk, etc. We believe that this information supplements on-going MS research and could be used to develop personalized disease-modifying therapies and exercises.","PeriodicalId":249670,"journal":{"name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125127092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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