A novel mobile-cloud system for capturing and analyzing wheelchair maneuvering data: A pilot study

Abstract

The purpose of this pilot study was to provide a new approach for capturing and analyzing wheelchair maneuvering data, which are critical for evaluating wheelchair users’ activity levels. We proposed a mobile-cloud (MC) system, which incorporated the emerging mobile and cloud computing technologies. The MC system employed smartphone sensors to collect wheelchair maneuvering data and transmit them to the cloud for storage and analysis. A K-Nearest-Neighbor (KNN) machine-learning algorithm was developed to mitigate the impact of sensor noise and recognize wheelchair maneuvering patterns. We conducted 30 trials in an indoor setting, where each trial contained 10 bouts (i.e., periods of continuous wheelchair movement). We also verified our approach in a different building. Different from existing approaches that require sensors to be attached to wheelchairs’ wheels, we placed the smartphone into a smartphone holder attached to the wheelchair. Experimental results illustrate that our approach correctly identified all 300 bouts. Compared to existing approaches, our approach was easier to use while achieving similar accuracy in analyzing the accumulated movement time and maximum period of continuous movement (p > 0.8). Overall, the MC system provided a feasible way to ease the data collection process, and generated accurate analysis results for evaluating activity levels.

Measurement of Physical Activity and Energy Expenditure in Wheelchair Users: Methods, Considerations and Future Directions

“…Researchers have explored attaching a custom data logger [ 45 ] or biaxial [ 46 ] and tri-axial [ 47 ] accelerometers onto the wheels of a wheelchair. Other preliminary research has simply attached a smartphone (containing a gyroscope and accelerometer) onto the armrest of a wheelchair [ 48 , 49 ]. Considering the exponential growth of smartphone ownership [ 50 ], this later approach in particular can widely be used to capture certain mobility characteristics such as average speed and distance travelled, functioning in a similar manner to pedometers in persons who do not use wheelchairs.…”

Validation of Using Smartphone Built-In Accelerometers to Estimate the Active Energy Expenditures of Full-Time Manual Wheelchair Users with Spinal Cord Injury

“…Although other studies have examined the use of smartphones with built-in accelerometers to measure EE [ 36 ], no previous study has investigated the validation of these devices for manual wheelchair users with SCI. The scant literature that is available on this population has focused on identifying wheelchair movements using the sensor data of a smartphone attached to the wheelchair, and then attempting to extrapolate the level of physical activity based on wheelchair movements [ 37 , 38 ].…”

On Human-in-the-Loop CPS in Healthcare: A Cloud-Enabled Mobility Assistance Service

“…28 Assistive mobility devices are beginning to benefit from cloud computing concepts. In Fu et al, 29 smart phone sensors collect navigational information of a wheelchair and transmit them to the cloud for storage. In Salhi et al, 30 robotics concepts are integrated to the cloud to share multiple wheelchair positions and the map of the environment to feed the wheelchairs' local controllers.…”