A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring

Characteristics of physical activity are indicative of one’s mobility level, latent chronic diseases and aging process. Accelerometers have been widely accepted as useful and practical sensors for wearable devices to measure and assess physical activity. This paper reviews the development of wearable accelerometry-based motion detectors. The principle of accelerometry measurement, sensor properties and sensor placements are first introduced. Various research using accelerometry-based wearable motion detectors for physical activity monitoring and assessment, including posture and movement classification, estimation of energy expenditure, fall detection and balance control evaluation, are also reviewed. Finally this paper reviews and compares existing commercial products to provide a comprehensive outlook of current development status and possible emerging technologies

Validation of smartphone step count algorithm used in STARFISH smartphone application

BACKGROUND: Smartphone sensors are underutilised in rehabilitation. OBJECTIVE: To validate the step count algorithm used in the STARFISH smartphone application. METHODS: Twenty-two healthy adults (8 male, 14 female) walked on a treadmill for 5 minutes at 0.44, 0.67, 0.90 and 1.33 m⋅s-1 . Each wore an activPAL TM and four Samsung Galaxy S3TM smartphones, with the STARFISH application running, in: 1) a belt carrycase, 2) a trouser or skirt pocket), 3a) a handbag on shoulder for females or 3b) shirt pocket for males and 4) an upper arm strap. Step counts of the STARFISH application and the activPALTM were compared at corresponding speeds and Bland-Altman statistics used to assess level of agreement (LOA). RESULTS: The LOA between the STARFISH application and activPALTM varied across the four speeds and positions, but improved as speed increased. The LOA ranged from 105–177% at 0.44 m⋅s-1; 50–98% at 0.67 m⋅s-1; 19–67% at 0.9 m⋅s-1 and 8–53% at 1.33 m⋅s-1. The best LOAs were at 1.33 m⋅s-1 in the shirt pocket (8%) and upper arm strap (12%) positions. CONCLUSIONS: Step counts measured by the STARFISH smartphone application are valid in most body positions especially at walking speeds of 0.9 m⋅s-1 and above

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Step detection and activity recognition accuracy of seven physical activity monitors

The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications

Measurement of Daily Energy Expenditure in Individuals with Chronic Heart Failure

Being able to accurately monitor and quantify the amount of physical activity an individual with chronic heart failure (CHF) performs can be of assistance in developing appropriate interventions. This thesis attempted to evaluate the validity of the RT3 accelerometer (RT3) and 7-day Physical Activity Recall Questionnaire (7 day PAR) in measuring the daily activity levels of community dwelling individuals with CHF. Fifty-four individuals with CHF participated in a 7 day session to estimate their daily physical activity by using the RT3 accelerometer and 7-day PAR questionnaire. In addition, 15 of the 54 individuals participated in a second study in with the validity of RT3was compared to oxygen consumption (VO2) measured by a MedGraphics VO2000 gas analyzer during typical daily activities. Although there was no significant difference between the RT3 and VO2 on mean caloric (Kcal) expenditure (p=0.67), the accelerometer tended to underestimate the energy expenditure (EE) and its validity was affected by activity intensity, movement patterns, placement location and soft tissue adhesion. The 7-day PAR overestimated EE by 22%, when compared to the RT3. There was no significant difference in the outcomes if the 7-day PAR only focused on day time activity versus 24-hour activity which included sleep. The second study revealed that the resting metabolic rate in individuals with CHF is significantly lower than 3.5 ml/kg/min, which indicates this metabolic constant for the general population is probably not appropriate for estimating daily energy expenditure in individuals with CHF

Prediction of Kick Count in Triathletes during Freestyle Swimming Session Using Inertial Sensor Technology

Monitoring sports training performances with automatic, low cost, low power, and ergonomic solutions is a topic of increasing importance in the research of the last years. A parameter of particular interest, which has not been extensively dealt with in a state-of-the-art way, is the count of kicks during swimming training sessions. Coaches and athletes set the training sessions to optimize the kick count and swim stroke rate to acquire velocity and acceleration during swimming. In regard to race distances, counting kicks can influence the athlete’s performance. However, it is difficult to record the kick count without facing some issues about subjective interpretation. In this paper, a new method for kick count is proposed, based on only one triaxial accelerometer worn on the athlete’s ankle. The algorithm was validated on data recorded during freestyle training sessions. An accuracy of 97.5% with a sensitivity of 99.3% was achieved. The proposed method shows good linearity and a slope of 1.01. These results overcome other state-of-the-art methods, proving that this method is a good candidate for a reliable, embedded kick count

Fall Detection Analysis Using a Real Fall Dataset

International Conference on Soft Computing Models in Industrial and Environmental Applications (13th. 2018. San Sebastián

A Discussion on Fall Detection Issues and Its Deployment: When cloud meets battery

IEEE International Conference on Cloud Computing and Big Data Analysis (3rd. 2018., Chengdu, China

The Estimation of Caloric Expenditure Using Three Triaxial Accelerometers

Accelerometer-based activity monitors are commonly used to measure physical activity energy expenditure (PAEE). Newly designed wrist and hip-worn triaxial accelerometers claim to accurately predict PAEE across a range of activities. Purpose: To determine if the Nike FuelBand (NFB), Fitbit (FB) and ActiGraph GT3X+ (AG) estimate PAEE in various activities. Methods: 21 healthy, college-aged adults wore a NFB on the right wrist, a FB on the left hip, and AG on the right hip, while performing 17 activities. AG data were analyzed using Freedson’s kcal regression equation. PAEE was measured using the Cosmed K4b2 (K4). Repeated measures ANOVAs were used to compare mean differences in PAEE (kcal/min). Paired sample t-tests with Bonferroni adjustments were used to locate significant differences. Results: For each device, the mean difference in PAEE was significantly different from the K4 (NFB, -0.45 + 2.8, FB, 0.48 + 2.27, AG, 0.64 + 2.59 kcal/min, p = 0.01). The NFB significantly overestimated most walking activities (e.g., regular walking; K4, 3.1 + 0.2 vs. NFB, 4.6 + 0.2 kcal/min) and activities with arm movements (e.g., sweeping; K4, 3.0 + 0.8 vs. NFB, 4.7 + 0.4 kcal/min, p \u3c 0.05). The NFB trended towards overestimating sport activities (basketball; K4, 10.8 + 0.8 vs. NFB, 12.2 + 0.5 kcal/min) (racquetball; K4, 9.6 + 0.8 vs. NFB 11.1 + 0.5 kcal/min). The FB and the AG significantly overestimated walking (K4, 3.1 + 0.2; FB, 5.4 + 0.3, AG, 5.8 + 0.4 kcal/min, p = 0.01) and underestimated PAEE of most activities with arm movements (e.g., Air Dyne, K4 5.6 + 0.2; Fitbit, 0.3 + 0.2; AG, 0.2 + 0.1 kcal/min, p \u3c 0.05) (racquetball, K4, 9.6 + 0.8 kcal/minute vs. FB, 7.4 + 0.6 kcal/minute, vs. AG, 6.5 + 0.4 kcal/minute, p \u3c 0.05). Conclusion: The NFB overestimated PAEE during most activities with arm movements and tended to overestimate sport activities, while the AG and FB overestimated walking and underestimated activities with arm movements. Overall, the wrist-worn NFB had similar accuracy to the waist-worn triaxial accelerometers; however, none of the devices were able to estimate PAEE across a range of activities